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Human gait recognition (HGR) leverages unique gait patterns to identify individuals, but the effectiveness of this technique can be hindered due to various factors such as carrying conditions, foot shadows, clothing variations, and changes in viewing angles. Traditional silhouette-based systems often neglect the critical role of instantaneous gait motion, which is essential for distinguishing individuals with similar features. We introduce the "Enhanced Gait Feature Extraction Framework (GaitSTAR)", a novel method that incorporates dynamic feature weighting through the discriminant analysis of temporal and spatial features within a channel-wise architecture. Key innovations in GaitSTAR include dynamic stride flow representation (DSFR) to address silhouette distortion, a transformer-based feature set transformation (FST) for integrating image-level features into set-level features, and dynamic feature reweighting (DFR) for capturing long-range interactions. DFR enhances contextual understanding and improves detection accuracy by computing attention distributions across channel dimensions. Empirical evaluations show that GaitSTAR achieves impressive accuracies of 98.5%, 98.0%, and 92.7% under NM, BG, and CL conditions, respectively, with the CASIA-B dataset; 67.3% with the CASIA-C dataset; and 54.21% with the Gait3D dataset. Despite its complexity, GaitSTAR demonstrates a favorable balance between accuracy and computational efficiency, making it a powerful tool for biometric identification based on gait patterns. [ABSTRACT FROM AUTHOR]

The accurate recognition of a brain tumor (BT) is crucial for accurate diagnosis, intervention planning, and the evaluation of post-intervention outcomes. Conventional methods of manually identifying and delineating BTs are inefficient, prone to error, and time-consuming. Subjective methods for BT recognition are biased because of the diffuse and irregular nature of BTs, along with varying enhancement patterns and the coexistence of different tumor components. Hence, the development of an automated diagnostic system for BTs is vital for mitigating subjective bias and achieving speedy and effective BT segmentation. Recently developed deep learning (DL)-based methods have replaced subjective methods; however, these DL-based methods still have a low performance, showing room for improvement, and are limited to heterogeneous dataset analysis. Herein, we propose a DL-based parallel features aggregation network (PFA-Net) for the robust segmentation of three different regions in a BT scan, and we perform a heterogeneous dataset analysis to validate its generality. The parallel features aggregation (PFA) module exploits the local radiomic contextual spatial features of BTs at low, intermediate, and high levels for different types of tumors and aggregates them in a parallel fashion. To enhance the diagnostic capabilities of the proposed segmentation framework, we introduced the fractal dimension estimation into our system, seamlessly combined as an end-to-end task to gain insights into the complexity and irregularity of structures, thereby characterizing the intricate morphology of BTs. The proposed PFA-Net achieves the Dice scores (DSs) of 87.54%, 93.42%, and 91.02%, for the enhancing tumor region, whole tumor region, and tumor core region, respectively, with the multimodal brain tumor segmentation (BraTS)-2020 open database, surpassing the performance of existing state-of-the-art methods. Additionally, PFA-Net is validated with another open database of brain tumor progression and achieves a DS of 64.58% for heterogeneous dataset analysis, surpassing the performance of existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multilayer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer is a worldwide fatal disease that exists mostly among women. The deep learning technique has proven its effectiveness, but the performance of the existing deep learning systems is quite compromising. In this work, a deep transfer learning system is suggested for efficient breast cancer classification from histopathology images. This system is based on a novel multi‐level progressive feature aggregation (MPFA) and a spatial domain learning approach. The combination of a pretrained Resnet101 backbone network with MPFA is implemented to extract more significant features. In addition, a mixed‐dilated spatial domain learning network (MSLN) is further incorporated to enhance the receptive field and increase discrimination between features. The proposed method achieved superior performance as compared to the existing state‐of‐the‐art methods, offering 99.24% accuracy, a 98.79% F‐1 score, 98.59% precision, and 98.99% recall values over BreaKHis dataset. An ablation study is carried out over the ICIAR2018 dataset to verify the generalizability and effectiveness of the system. [ABSTRACT FROM AUTHOR]

The goal of RGB-D salient object detection is to aggregate the information of the two modalities of RGB and depth to accurately detect and segment salient objects. Existing RGB-D SOD models can extract the multilevel features of single modality well and can also integrate cross-modal features, but it can rarely handle both at the same time. To tap into and make the most of the correlations of intra- and inter-modality information, in this paper, we proposed an attention-guided cross-modal multi-feature aggregation network for RGB-D SOD. Our motivation was that both cross-modal feature fusion and multilevel feature fusion are crucial for RGB-D SOD task. The main innovation of this work lies in two points: One is the cross-modal pyramid feature interaction (CPFI) module that integrates multilevel features from both RGB and depth modalities in a bottom-up manner, and the other is cross-modal feature decoder (CMFD) that aggregates the fused features to generate the final saliency map. Extensive experiments on six benchmark datasets showed that the proposed attention-guided cross-modal multiple feature aggregation network (ACFPA-Net) achieved competitive performance over 15 state of the art (SOTA) RGB-D SOD methods, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Mangrove forests are significant participants in coastal ecological environment systems. For the development of protection strategies, it is crucial to automatically and accurately detect the distribution and area of mangroves using satellite images. Although many deep-learning-based mangrove detection and segmentation algorithms have made notable progress, the complex regional structures and the great similarity between mangroves and the surrounding environment, as well as the diversity of mangroves, render the task still challenging. To cover these issues, we propose a novel deep-supervision-guided feature aggregation network for mangrove detection and segmentation called MangroveSeg, which is based on a U-shaped structure with ResNet, combining an attention mechanism and a multi-scale feature extraction framework. We also consider the detection and segmentation of mangroves as camouflage detection problems for the improvement and enhancement of accuracy. To determine more information from extracted feature maps in a hidden layer, a deep supervision model is introduced in up-sampling to enhance feature representation. The spatial attention mechanism with attention gates is utilized to highlight significant regions and suppress task-independent feature responses. The feature fusion module can obtain multi-scale information by binding each layer to the underlying information and update feature mappings. We validated our framework for mangrove detection and segmentation using a satellite image dataset, which includes 4000 images comprising 256 × 256 pixels; we used 3002 for training and 998 for testing. The satellite images dataset was obtained from the Dongzhaigang National Nature Reserve located in Haikou City, Hainan Province, China. The proposed method achieved a 89.58% overall accuracy, 89.02% precision, and 80.7% mIoU. We also used the trained MangroveSeg model to detect mangroves on satellite images from other regions. We evaluated the statistical square measure of some mangrove areas and found that the evaluation accuracy can reach 96% using MangroveSeg. The proposed MangroveSeg model can automatically and accurately detect the distribution and area of mangroves from satellite images, which provides a method for monitoring the ecological environment. [ABSTRACT FROM AUTHOR]

Learning is an essential requirement for humans, and its means have evolved. Ten years ago, Massive Open Online Courses (MOOCs) were introduced, attracting many interests and learners. MOOCs provide forums for learners to interact with instructors and to express any problems they encounter in the educational process. However, MOOCs have a high dropout rate due to the difficulties of following up on learners' posts and identifying the urgent ones to react quickly. This research aims to assist instructors in automatically identifying urgent posts, making it easier to respond to such posts rapidly, increasing learner engagement, and improving course completion rate. In this paper, we propose a novel classification model for identifying urgent posts. The proposed model consists of four stages. In the first stage, the post-text is code-encoded and vectorized using a pre-trained BERT model. In the second stage, a novel feature aggregation model is proposed to reveal data-based relationships between token features and their representation in a higher-level feature. In the third stage, a novel model based on convolutional neural networks (CNNs) is proposed to reveal the meaning of a text context more accurately. In the last stage, the extracted composite features are used to classify the text of the post. Several experimental studies were conducted to get the best performance of the proposed stages of the system. The experimental results demonstrated the architectural efficiency of the proposed feature aggregation and multiple CNN models, as well as the accuracy of the proposed system compared to the current research. [ABSTRACT FROM AUTHOR]

Developing computationally efficient semantic segmentation models that are suitable for resource-constrained mobile devices is an open challenge in computer vision research. To address this challenge, we propose a novel real-time semantic scene segmentation model called Multi-encoder Context Aggregation Network (MCANet), which offers the best combination of low model complexity and state-of-the-art (SOTA) performance on benchmark datasets. While we follow the multi-encoder approach, our novelty lies in the varying number of scales to capture both global context and local details effectively. We introduce suitable lateral connections between sub-encoders for improved feature refinement. We also optimize the backbone by exploiting the residual block of MobileNet for resource-constrained applications. On the decoder side, the proposed model includes a new Local and Global Context Aggregation (LGCA) module that significantly enhances semantic details in the segmentation output. Finally, we use several known efficient convolution techniques for the classification module to make the model more computationally efficient. We provide a comprehensive evaluation of MCANet on multiple datasets containing structured and unstructured urban street scenes. Among the existing real-time models with less than 3 million parameters, the proposed model is more competitive as it achieves the SOTA performance without ImageNet pre-trained weights on both structured and unstructured environments while being more compact for resource-constrained applications.

Building footprint extraction from remotely sensed imagery is a critical task in the field of illegal building discovery, urban dynamic monitoring, and disaster emergency response. Recent research has made significant progress in this area by utilizing deep learning techniques. However, it remains difficult to efficiently balance the spatial detail and rich semantic features. In particular, the extracted building edge is often inaccurate, especially in areas where the buildings are densely distributed, and the boundary of adjacent building instances is difficult to distinguish accurately. Additionally, identifying buildings with varying scales remains a challenging problem. To address the above problems, we designed a novel framework that aggregated multilevel contextual information extracted from multiple encoders. Furthermore, we introduced an instance constraint into contrastive learning to enhance the robustness of the feature representation. Experimental results demonstrated that our proposed method achieved 91.07% and 74.58% on the intersection over union metric on the WHU and Massachusetts datasets, respectively, outperforming the most recent related methods. Notably, our method significantly improved the accuracy of building boundaries, especially at the building instance level, and the integrity of multi-scale buildings. [ABSTRACT FROM AUTHOR]

Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. This paper proposes a novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks. FARNet employs an encoder-decoder structure architecture. To alleviate the problem of limited training data in the medical domain, we adopt a backbone network pre-trained on natural images as the encoder. The decoder includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement module for high-resolution heatmap regression. Coarse-to-fine supervisions are applied to the two modules to facilitate end-to-end training. We further propose a novel loss function named Exponential Weighted Center loss for accurate heatmap regression, which focuses on the losses from the pixels near landmarks and suppresses the ones from far away. We evaluate FARNet on three publicly available anatomical landmark detection datasets, including cephalometric, hand, and spine radiographs. Our network achieves state-of-the-art performances on all three datasets. Code is available at https://github.com/JuvenileInWind/FARNet. [ABSTRACT FROM AUTHOR]

Fabric defect detection plays a critical role for measuring quality control in the textile manufacturing industry. Deep learning-based saliency models can quickly spot the most interesting regions that attract human attention from the complex background, which have been successfully applied in fabric defect detection. However, most of the previous methods mainly adopted multi-level feature aggregation yet ignored the complementary relationship among different features, and thus resulted in poor representation capability for the tiny and slender defects. To remedy these issues, we propose a novel saliency-based fabric defect detection network, which can exploit the complementary information between different layers to enhance the representation features ability and discrimination of defects. Specifically, a multiscale feature aggregation unit (MFAU) is proposed to effectively characterize the multi-scale contextual features. Besides, a feature fusion refinement module (FFR) composed of an attention fusion unit (AFU) and an auxiliary refinement unit (ARU) is designed to exploit complementary important information and further refine the input features for enhancing the discriminative ability of defect features. Finally, a multi-level deep supervision (MDS) is adopted to guide the model to generate more accurate saliency maps. Under different evaluation metrics, our proposed method outperforms most state-of-the-art methods on our developed fabric datasets. [ABSTRACT FROM AUTHOR]

Recently, point-based networks have begun to prevail because they retain more original geometric information from point clouds than other deep learning-based methods. However, we observe that: (1) the set abstraction design for local aggregation in point-based networks neglects that the points in a local region may belong to different semantic categories, and (2) most works focus on single-scale local features while ignoring the importance of multi-scale global features. To tackle the above issues, we propose two novel strategies named semantic-based local aggregation (SLA) and multi-scale global pyramid (MGP). The key idea of SLA is to augment local features based on the semantic similarity of neighboring points in the local region. Additionally, we propose a hierarchical global aggregation (HGA) module to extend local feature aggregation to global feature aggregation. Based on HGA, we introduce MGP to obtain discriminative multi-scale global features from multi-resolution point cloud scenes. Extensive experiments on two prevailing benchmarks, S3DIS and Semantic3D, demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Recently, deep learning algorithms have been successfully applied to industrial fault detection because they are better at automatically extracting complex features and processing high-dimensional data than traditional methods. However, most existing deep learning-based fault detection methods only concentrate on extracting features from industrial process data without considering the crucial long-term temporal features and higher-order statistical information. To address this challenge, we proposed a novel enhanced higher-order pooling-based network (EHOPN) for industrial fault detection. First, the data pre-processing of the network is presented to capture the dynamic features of time-series process data and unify the high-dimensional data scale. Second, the EHOPN utilizes channel and temporal second-order pooling techniques to gather temporal and channel statistics information, facilitating the backbone network's ability to capture complex inter-dependencies and long-term dynamics. Additionally, the high-order feature aggregation module is presented to aggregate global and local features, enhancing the network's generalization ability. The proposed industrial fault detection approach is evaluated on the Tennessee Eastman benchmark and a real-world heavy-plate production process. Experimental results show that the proposed method is significantly better than comparison models in four evaluation metrics: accuracy, precision, recall, and F1-score, further proving the effectiveness of EHOPN. [ABSTRACT FROM AUTHOR]

Automatic extraction of power transmission lines from remote sensing images is important for intelligent power inspection. However, this task faces many challenges, such as complex backgrounds and varying illumination, which make accurate segmentation difficult. Additionally, the severe class imbalance issue arising from the smaller pixel proportion of transmission lines compared to background makes segmentation even more challenging. To address these challenges, a multi-attention fusion network, named MAF-Net, is put forward to achieve accurate end-to-end segmentation of remote sensing images of transmission lines. Firstly, aimed at the inadequate process of local features, a multi-scale context fusion (MCF) block is put forward for effective feature aggregation of multi-scale features. Meanwhile, a dense block is also introduced for feature enhancement to acquire multi-scale contexts. Secondly, considering the pooling operation will bring the problem of information loss, a three-branch pooling (TP) block is proposed to take the best of the advantages of different pooling operations. In addition, the multi-scale input unit is also proposed to support deep layers for extracting the discriminative context information. Furthermore, to tackle the class imbalance issue, a multi-attention block (MA) is used to optimize the simple skip connections to fully aggregate spatial attention, pixel attention, and channel attention features, allowing this segmentation model to focus on the key areas containing power transmission lines. Comparison experiments and ablation experiments are carried out on the remote aerial sensing images for performance analysis. Experiments indicate that proposed MAF-Net could achieve a superior segmentation ability. • A multi-attention fusion network is proposed for accurate power line extraction. • A multi-scale context fusion block is proposed for effective feature learning. • A hybrid feature pooling block is to address the information loss issue. • Proposed model achieves a competitive performance on multiple public sets. [ABSTRACT FROM AUTHOR]

Bridge crack is one of the critical optical and visual information to judge the health state of bridges. The bridge crack detection methods based on artificial intelligence are essential in this field, but the current approaches are not satisfactory in terms of speed and accuracy. This study proposes a novel multi-scale crack detection network, called MSCNet, comprising a texture enhancement mechanism and feature aggregation to enhance the visual saliency of the objects in the background for bridge crack detection. We use Res2Net as the backbone network to improve the depth information expression ability of the cracks itself. Because the edge property of bridge cracks is prominent, to make full use of this visual feature, we use a texture enhancement module based on group attention to capturing the detailed information of cracks in low-level features. To further mine the depth information of the network, we use a cascade fusion module to capture crack location information in high-level features. Finally, to fully utilize the characteristic information of the deep network, we fuse the low- and high-level features to obtain the final crack prediction. We evaluate the proposed method compared with other state-of-the-art methods on a large-scale crack dataset. The experimental results demonstrate the effectiveness and superiority of the proposed method, which achieves a precision of 93.5%, recall of 94.2%, and inference speed of over 63 FPS.

Remote sensing image change detection is an important technology in remote sensing data analysis. Existing mainstream solutions are divided into supervised and unsupervised solutions. Among supervised methods, most remote sensing image change methods based on deep learning are related to semantic segmentation. However, these methods only use deep learning model to extract and process single-level information of the image, and do not separately train the changed and unchanged areas according to the task characteristics of change detection. Also this model do not aggregate the deep and shallow-level feature information, so many local change details cannot be detected. This paper proposes a multilateral feature-guided aggregation network. The network firstly extracts the whole information, the changed area information and the unchanged area information of remote sensing image through the main network and two auxiliary networks: difference network and assimilation network. Then, the whole information of the image is aggregated with the changed and unchanged region information by feature aggregation network (all subnetworks are composed of convolutional neural networks (CNNs)). The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately. [ABSTRACT FROM AUTHOR]

Detection and segmentation of cell nuclei in hematoxylin and eosin-stained tissue images pose critical clinical challenges, including intricate backgrounds, variable nuclear appearances, overlapping nuclei, and unclear boundaries. These complexities render automated instance segmentation a difficult research area. Thus, this paper proposes a deep aggregation transformer network for automatic nucleus instance segmentation. The method employs a robust Vision Transformer-based backbone to extract informative features from images. To fuse multi-level information, we propose the group aggregation multi-level feature fusion module, which merges low-level features for shape representation and high-level features for pixel edge segmentation, enhancing overall model performance. The depthwise integrated multilayer perceptron module enhances feature learning by combining the global context-dependent representation of MLP and the local representation of convolution through channel separation. This fosters feature learning across different modes, thereby improving network representation. The bidirectional path interaction feature processing module is introduced for multi-scale information extraction and fusion. This module enhances the entire feature pyramid hierarchy by transferring deep semantic information to shallow layers with robust spatial information via bidirectional paths. The Large kernel Attention module at each layer extensively improves feature extraction through a large receptive field. Our approach outperforms state-of-the-art models, including StarDIST, CPP-Net, TSFD-Net and TransNuSeg, by 7.8%, 6.3%, 1.5% and 3.5% in average panoptic quality for different cell nuclei segmentation categories across tissues of the Pannuke dataset. Furthermore, DAT-Net outperforms all other state-of-the-art models in terms of the F1 score for the five classes of cell nuclei in the PanNuke dataset. • We innovatively employ the Vision Transformer for cell nucleus instance segmentation tasks. • We design a new feature fusion module, group aggregation multi-level feature fusion module. • We develop an integrated learning module, deepwise integrate multilayer perceptron module. • We construct a bidirectional path structure with large kernel attention mechanism for efficient feature extraction. • DAT-Net has notably enhanced segmentation performance and efficiency across two widely used datasets. [ABSTRACT FROM AUTHOR]

The issue of drug safety has received increasing attention in modern society. Estimating the frequency of drug side effects proves to be an effective approach to improving drug development safety. Clinical trials are the most widely used manner in the medical field, but their long duration and high labor costs are always challenging for researchers. Many drug side effect frequency prediction methods based on graph neural networks have also been proposed and achieved good results. However, most current mainstream methods extract feature information separately for drugs and side effects but fail to capture their interaction information, which seriously degenerates the final prediction performance. To solve this problem, we have proposed a network to explore the underlying relationship between drugs and the frequency of side effects by combining graph attention learning, cross attention interaction, and feature aggregation into a unified framework. In this network, we first use the graph attention mechanism to accomplish effective feature extraction for drugs and side effects, respectively. Then, by designing a cross-attention interaction module, the chemical characteristics of each atom of the drug and the correlation between the side effects are captured to gather information on the interaction between the drug and the side effects. Subsequently, we fully fuse graph attention features and interaction attention features by embedding a feature fusion module to obtain enhanced features that fuse drugs and side effects. Finally, the predicted frequency is obtained using matrix inner product operation. Experimental results on the SIDER dataset show that our proposed method achieves the best performance when compared to previous state-of-the-art methods in both warm start and cold start scenarios. We also conducted ablation experiments to demonstrate the effectiveness of different modules embedded in the network. The code, datasets, and materials are available at https://github.com/zixiaojin66/A-3Net-master. In addition, we construct a user-friendly web server for testing at: https://a3net666.streamlit.app/. • We propose a deep learning model for predicting the frequency of drug side effects. • The CAM and GAM effectively perform feature extraction for drugs and side effects. • Experimental results demonstrate its superiority over state-of-the-art models. [ABSTRACT FROM AUTHOR]

Histopathological analysis is the gold standard for cancer diagnosis, but traditional manual identification methods are time-consuming, labor-intensive, and highly relied on the experience of pathologists. In recent years, deep learning methods can assist pathologists to achieve accurate and automated detection of lesion areas. However, lesion detection is a difficult task due to its various size, shape, and surrounding complex scenes. Existing methods ignore the global interaction between lesion area and normal area, so it is insufficient to extract important global features. In addition, most methods fail to effectively fuse deep and shallow features, resulting in the extracted features lacking discrimination, and these deficiencies lead to incomplete extraction and irregular boundaries of lesion. To address the above-mentioned issues, an end-to-end histopathological image segmentation network based on global context-aware module and deep feature aggregation (GCMDFA) is proposed for segmenting lesions in histopathological images (HIs). In the GCMDFA, a global context-aware module is applied to better explore the interactive relationship between any two pixels to directly capture global contextual information related to lesion tissue of HIs for achieving global features. To better decode the features of encoder, a deep feature aggregation structure is designed to effectively fuse deep and shallow layer features of HIs. Experimental results on three HI datasets (including public and external data) show that the proposed GCMDFA method is more competitive compared with some state-of-the-art (SOTA) methods. This research can assist pathologists to analyze HIs in clinical computer-aided diagnosis. • A GCMDFA method is developed for lesion segmentation of histopathological images. • GCM is applied to capture global contextual information related to lesions. • DFA is designed to fuse deep and shallow layer features of HIs. • Experiments on three datasets show that our method is more competitive. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human Action Recognition (HAR) is a highly notable area of study in contemporary computer vision. Many investigations focused on recognizing a person's actions from video streams based on extracting features regarding orientation and motion. This article presents a Joints and Trajectory-pooled 3D-Deep Positional Attention (PA)-based Hierarchical Bidirectional Recurrent Convolutional Descriptors (JTDPAHBRD) approach which uses a PA-based Hierarchical Bidirectional Recurrent Neural Network (PAHBRNN) for enhancing the feature aggregation process. First, the entire video is segregated into multiple blocks and they are provided to the 2-stream bilinear Convolutional 3D (C3D) model which applies the PAHBRNN as feature aggregation. In PAHBRNN, the feature vectors related to the different parts of a human skeleton in a certain clip are hierarchically aggregated using the position-aware guidance vector. Then, 2 different streams in the C3D network are fused and trained end-to-end using the softmax loss to get the final video descriptor for a particular video sequence. Further, the Support Vector Machine (SVM) classifier is applied to classify the resultant video descriptor to recognize the person's actions. At last, the investigational outcomes demonstrate the JTDPAHBRD achieves 99.6% better recognition accuracy than the classical state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Face detection generally requires prior boxes and an extra non-maximum suppression(NMS) post-processing in modern deep learning methods. However, anchor design and anchor matching strategy significantly affect the performance of face detectors, so we have to spend a lot of time on anchor designing for different business scenarios. The other issue is that NMS cannot be easily parallelized and it may become a bottleneck of detection speed. In this paper, we propose a simple yet efficient pure convolutional neural network face detection method, named dual-branch center face detector(DBCFace for short), which solve face detection via a dual branch fully convolutional framework without extra anchor design and NMS. Extensive experiments are conducted on four popular face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE, demonstrating that our method is comparable with state-of-the-art methods while the speed is faster. [ABSTRACT FROM AUTHOR]

Models based on capsule neural network (CapsNet), a novel deep learning method, have recently made great achievements in hyperspectral remote sensing image (HSI) classification due to their excellent ability to implicitly model the spatial relationship knowledge embedded in HSIs. However, the number of labeled samples is a common bottleneck in HSI classification, limiting the performance of these deep learning models. To alleviate the problem of limited labeled samples and further explore the potential of CapsNet in the HSI classification field, this study proposes a multiscale feature aggregation capsule neural network (MS-CapsNet) based on CapsNet via the implementation of two branches that simultaneously extract spectral, local spatial, and global spatial features to integrate multiscale features and improve model robustness. Furthermore, because deep features are generally more discriminative than shallow features, two kinds of capsule residual (CapsRES) blocks based on 3D convolutional capsule (3D-ConvCaps) layers and residual connections are proposed to increase the depth of the network and solve the limited labeled sample problem in HSI classification. Moreover, a squeeze-and-excitation (SE) block is introduced in the shallow layers of MS-CapsNet to enhance its feature extraction ability. In addition, a reasonable initialization strategy that transfers parameters from two well-designed, pretrained deep convolutional capsule networks is introduced to help the model find a good set of initializing weight parameters and further improve the HSI classification accuracy of MS-CapsNet. Experimental results on four widely used HSI datasets demonstrate that the proposed method can provide results comparable to those of state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The recording process of observation is influenced by multiple factors, such as viewpoint, illumination, and state of the object-of-interest etc.Thus, the image observation of the same object may vary a lot under different conditions. This leads to severe intra-class variance which greatly challenges the discrimination ability of the vision model. However, the current prevailing softmax loss for visual recognition only pursues perfect inter-class separation in the feature space. Without considering the intra-class compactness, the learned model easily collapses when it encounters the instances that deviate a lot from their class centroid. To resist the intra-class variance, we start by organizing the input instances as a graph. From this viewpoint, we find that the normalized cut on the graph is a favorable surrogate metric of the intra-class variance within the training batch. Inspired by the equivalence between the normalized cut and random walk, we propose a feature aggregation scheme using transition probabilities as guidance. By imposing supervision on the aggregated features, we can constrain the transition probabilities to form a graph partition consistent with the given labels. Thus, the normalized cut as well as intra-class variance can be well suppressed. To validate the effectiveness of this idea, we instantiate it in spatial, temporal, and spatial-temporal scenarios. Experimental results on corresponding benchmarks demonstrate that the proposed feature aggregation leads to significant improvement in performance. Our method is on par with, or even better than current state-of-the-arts in both tasks. [ABSTRACT FROM AUTHOR]

In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event from audio information, and the use of audio surveillance systems can greatly improve the robustness and reliability of event detection. In this paper, a novel system for the detection of polyphonic urban noise is proposed for on-campus audio surveillance. The system aggregates different acoustic features to improve the classification accuracy of urban noise. A combination model composed of a capsule neural network (CapsNet) and recurrent neural network (RNN) is employed as the classifier. CapsNet overcomes some limitations of convolutional neural networks (CNNs), such as the loss of position information after max-pooling, and the RNN mainly models the temporal dependency of context information. The combination of these networks further improves the accuracy and robustness of polyphonic sound events detection. Moreover, a monitoring platform is designed to visualize noise maps and acoustic event information. The deployment architecture of the system is used in real environments, and experiments were also conducted on two public datasets. The results demonstrate that the proposed method is superior to existing state-of-art methods for the polyphonic sound event detection task.

Multi-person pose estimation is a fundamental yet challenging task in computer vision. Although great success has been made in this field due to the rapid development of deep learning, complex situations (e.g., extreme poses, occlusions, overlapped persons, and crowded scenes) are still not well solved. To further mitigate these issues, we propose a novel Content-aware Feature Enhancement Network (CFENet), which consists of three effective modules: Feature Aggregation and Selection Module (FASM), Feature Fusion Module (FFM) and Dense Upsampling Convolution (DUC) module. The FASM includes Feature Aggregation Module (FAM) and Information Selection Module (ISM). The FAM constructs the hierarchical multi-scale feature aggregations in a granular level to capture more accurate fine-grained representations. The ISM makes the aggregated representations more distinguished, which adaptively highlights the discriminative human part representations both in the spatial location and channel context. Then, we perform FFM which effectively fuses high-resolution spatial features and low-resolution semantic features to obtain more reliable context information for well-estimated joints. Finally, we adopt DUC module to generate more precise prediction, which can recover missing joint details that are usually unavailable in common upsampling process. Comprehensive experiments demonstrate that the proposed approach outperforms most of the popular methods and achieves a competitive performance with the state-of-the-art methods over three benchmark datasets: the recent big dataset CrowdPose, the COCO keypoint detection dataset and the MPII Human Pose dataset. Our code will be released upon acceptance. [ABSTRACT FROM AUTHOR]

Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens. In this paper, we propose a new Adjacent Aggregation Network (A2Net) with lightweight architectures to remove raindrops from single images. Instead of directly cascading convolutional layers, we design an adjacent aggregation architecture to better fuse features for rich representations generation, which can lead to high quality images reconstruction. To further simplify the learning process, we utilize a problem-specific knowledge to force the network focus on the luminance channel in the YUV color space instead of all RGB channels. By combining adjacent aggregating operation with color space transformation, the proposed A2Net can achieve state-of-the-art performances on raindrop removal with significant parameters reduction.

Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.

Recently, point-based networks have begun to prevail because they retain more original geometric information from point clouds than other deep learning-based methods. However, we observe that: (1) the set abstraction design for local aggregation in point-based networks neglects that the points in a local region may belong to different semantic categories, and (2) most works focus on single-scale local features while ignoring the importance of multi-scale global features. To tackle the above issues, we propose two novel strategies named semantic-based local aggregation (SLA) and multi-scale global pyramid (MGP). The key idea of SLA is to augment local features based on the semantic similarity of neighboring points in the local region. Additionally, we propose a hierarchical global aggregation (HGA) module to extend local feature aggregation to global feature aggregation. Based on HGA, we introduce MGP to obtain discriminative multi-scale global features from multi-resolution point cloud scenes. Extensive experiments on two prevailing benchmarks, S3DIS and Semantic3D, demonstrate the effectiveness of our method.

• We propose a Local Graph Aggregation (LGA) module for CNN information diffusion. • We present the LABNet architecture with a cascade of LGA modules for vehicle re-id. • Our method outperforms state-of-the-art vehicle re-id methods on several datasets. Vehicle re-identification is an important computer vision task where the objective is to identify a specific vehicle among a set of vehicles seen at various viewpoints. Recent methods based on deep learning utilize a global average pooling layer after the backbone feature extractor, however, this ignores any spatial reasoning on the feature map. In this paper, we propose local graph aggregation on the backbone feature map, to learn associations of local information and hence improve feature learning as well as reduce the effects of partial occlusion and background clutter. Our local graph aggregation network considers spatial regions of the feature map as nodes and builds a local neighborhood graph that performs local feature aggregation before the global average pooling layer. We further utilize a batch normalization layer to improve the system effectiveness. Additionally, we introduce a class balanced loss to compensate for the imbalance in the sample distributions found in the most widely used vehicle re-identification datasets. Finally, we evaluate our method in three popular benchmarks and show that our approach outperforms many state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Detecting small-scale pedestrians in aerial images is a challenging task that can be difficult even for humans. Observing that the single image based method cannot achieve robust performance because of the poor visual cues of small instances. Considering that multiple frames may provide more information to detect such difficult case instead of only single frame, we design a novel video based pedestrian detection method with a two-stream network pipeline to fully utilize the temporal and contextual information of a video. An aggregated feature map is proposed to absorb the spatial and temporal information with the help of spatial and temporal sub-networks. To better capture motion information, a more refined flow net (SPyNet) is adopted instead of a simple flownet. In the spatial stream subnetwork, we modified the backbone network structure by increasing the feature map resolution with relatively larger receptive field to make it suitable for small-scale detection. Experimental results based on drone video datasets demonstrate that our approach improves detection accuracy in the case of small-scale instances and reduces false positive detections. By exploiting the temporal information and aggregating the feature maps, our two-stream method improves the detection performance by 8.48% in mean Average Precision (mAP) from that of the basic single stream R-FCN method, and it outperforms the state-of-the-art method by 3.09% on the Okutama Human-action dataset. [ABSTRACT FROM AUTHOR]

This letter proposes an attention-based convolutional neural network architecture for multitasking learning to accurately classify not only the presence of an earthquake but also the event type of the earthquake. In particular, to improve the performance in earthquake-type classification, we develop an attention-based feature aggregation framework embedded in multitask learning architecture. Representative experimental results show that the proposed method provides an effective structure for an earthquake detection and event classification with an earthquake database of the Korean peninsula and the Circum-Pacific belt. [ABSTRACT FROM AUTHOR]

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) aims to identify objects that are visually blended into their highly similar surroundings, which is an extremely complex and challenging visual task in real-world scenarios, and has recently attracted increasing research interest in the field of computer vision due to its valuable applications. The existing deep learning based methods of COD have the following problems: 1) the ambiguous boundary of the camouflaged objects in prediction map, 2) an inaccurate detection of the camouflaged object with accurate and complete structure details. To this end, an attention guided multi-level feature aggregation network is proposed for this task, which is based on three key designs. First, by embedding spatial pyramid attention (SPA) in ResNet-like backbone network, better multi-level features are extracted to identify the camouflaged objects with complete internal details. Second, an edge context module is designed to make full use of edge information, which highlights camouflaged object structure and generates accurate edge localization of COD. Third, the feature aggregation module based on local attention is used to fuse these enhanced multi-level features and edge context cues, which consists of two major components: the iterative Attentional Feature Fusion (iAFF) module and the Dual-branch Global Context Module (DGCM). Compared with the existing 16 state-of-the-art methods, extensive experiments on four widely-used benchmark datasets under four authoritative evaluation metrics illustrate that the proposed method is very beneficial to the COD task. • A ResNet-like backbone equiped with SPA-block is designed to get better initial features. • Several simple modules are used to boost features for more discriminative representation. • FAM module is designed to fuse features and edge cue in multiple supervision to get more accurate results. • Experiments and evaluations validate the effectiveness of the modules, and show superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis is essential for the safe and stable operation of rotating machinery. Existing methods use signals from a single dimension, limiting diagnostic generality and accuracy. To address these limitations and make improved use of signal features from multiple dimensions, a novel convolutional neural network model with multi-dimensional signal inputs and multi-dimensional task outputs called MIMTNet is proposed. First, frequency domain signals and a time frequency graph are obtained by using the short-time Fourier transform and a wavelet transform to process original time domain signals simultaneously. Then, the time domain signals, the frequency domain signals, and the time frequency graph are fed into the model and a special aggregation is performed after extracting features from the three corresponding branches. Finally, the outputs of the three-dimensional tasks are acquired by different full connection layers to process the aggregated features of bearing position, damage location within the bearing, and the damage size. Two common bearing vibration signal datasets are used to verify the generalization ability of our proposed method. And experimental results show that the proposed method effectively improves the bearing diagnosis capability of the deep learning model. [ABSTRACT FROM AUTHOR]

Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomplete mining and insufficient utilization of semantic information, and the edge information of segmentation is rough. To solve the above problems, we propose a multi-scale feature aggregation network. In order to improve the ability of the network to process boundary information, we design a deep feature extraction module using a multi-scale pyramid to extract features, combined with the designed attention mechanism and strip convolution, extraction of multi-scale deep semantic information and enhancement of spatial and location information. Then, the multi-branch aggregation module is used to interact with different scale features to enhance the positioning information of the pixels. Finally, the two high-performance branches designed in the Feature Fusion Upsample module are used to deeply extract the semantic information of the image, and the deep information is fused with the shallow information generated by the multi-branch module to improve the ability of the network. Global and local features are used to determine the location distribution of each image category. The experimental results show that the accuracy of the segmentation method in this paper is better than that in the previous detection methods, and has important practical significance for the actual water area segmentation.

Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first splitting a point cloud into some local regions, then extracting the corresponding feature of each local region, and finally aggregating all individual local region features into a global feature as shape representation using simple max-pooling. However, such pooling-based feature aggregation methods do not adequately take the spatial relationships (e.g. the relative locations to other regions) between local regions into account, which greatly limits the ability to learn discriminative shape representation. To address this issue, we propose a novel deep learning network, named Point2SpatialCapsule, for aggregating features and spatial relationships of local regions on point clouds, which aims to learn more discriminative shape representation. Compared with the traditional max-pooling based feature aggregation networks, Point2SpatialCapsule can explicitly learn not only geometric features of local regions but also the spatial relationships among them. Point2SpatialCapsule consists of two main modules. To resolve the disorder problem of local regions, the first module, named geometric feature aggregation, is designed to aggregate the local region features into the learnable cluster centers, which explicitly encodes the spatial locations from the original 3D space. The second module, named spatial relationship aggregation, is proposed for further aggregating the clustered features and the spatial relationships among them in the feature space using the spatial-aware capsules developed in this article. Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters. Experimental results demonstrate that Point2SpatialCapsule outperforms the state-of-the-art methods in the 3D shape classification, retrieval and segmentation tasks under the well-known ModelNet and ShapeNet datasets. [ABSTRACT FROM AUTHOR]

The goal of Multi-View Stereo (MVS) is to reconstruct 3D point-cloud model from multiple views. On the basis of the considerable progress of deep learning, an increasing amount of research has moved from traditional MVS methods to learning-based ones. However, two issues remain unsolved in the existing state-of-the-art methods: (1) only high-level information is considered for depth estimation. This may reduce the localization accuracy of 3D points as the learned model lacks spatial information; and (2) most of the methods require additional post-processing or network refinement to generate a smooth 3D model. This significantly increases the number of model parameters or the computational complexity. To this end, we propose MVSNet++, an end-to-end trainable network for dense depth estimation. Such an estimated depth map can further be applied to 3D model reconstruction. Different from previous methods, in the proposed method, we first adopt feature pyramid structures for both feature extraction and cost volume regularization. This can lead to accurate 3D point localization by fusing multi-level information. To generate smooth depth map, we then carefully integrate instance normalization into MVSNet++ without increasing model parameters and computational burden. Furthermore, we additionally design three loss functions and integrate Curriculum Learning framework into the training process, which can lead to an accurate reconstruction of 3D model. MVSNet++ is evaluated on DTU and Tanks & Temples benchmarks with comprehensive ablation studies. Experimental results demonstrate that our proposed method performs favorably against previous state-of-the-art methods, showing the accuracy and effectiveness of the proposed MVSNet++. [ABSTRACT FROM AUTHOR]

Recently, food recognition has received more and more attention in image processing and computer vision for its great potential applications in human health. Most of the existing methods directly extracted deep visual features via convolutional neural networks (CNNs) for food recognition. Such methods ignore the characteristics of food images and are, thus, hard to achieve optimal recognition performance. In contrast to general object recognition, food images typically do not exhibit distinctive spatial arrangement and common semantic patterns. In this paper, we propose a multi-scale multi-view feature aggregation (MSMVFA) scheme for food recognition. MSMVFA can aggregate high-level semantic features, mid-level attribute features, and deep visual features into a unified representation. These three types of features describe the food image from different granularity. Therefore, the aggregated features can capture the semantics of food images with the greatest probability. For that solution, we utilize additional ingredient knowledge to obtain mid-level attribute representation via ingredient-supervised CNNs. High-level semantic features and deep visual features are extracted from class-supervised CNNs. Considering food images do not exhibit distinctive spatial layout in many cases, MSMVFA fuses multi-scale CNN activations for each type of features to make aggregated features more discriminative and invariable to geometrical deformation. Finally, the aggregated features are more robust, comprehensive, and discriminative via two-level fusion, namely multi-scale fusion for each type of features and multi-view aggregation for different types of features. In addition, MSMVFA is general and different deep networks can be easily applied into this scheme. Extensive experiments and evaluations demonstrate that our method achieves state-of-the-art recognition performance on three popular large-scale food benchmark datasets in Top-1 recognition accuracy. Furthermore, we expect this paper will further the agenda of food recognition in the community of image processing and computer vision. [ABSTRACT FROM AUTHOR]

Timber structures have been widely utilized in the field of construction. Nevertheless, these structures are susceptible to environmental factors that can cause internal damage, including cavities, cracks, and corrosion, which can significantly compromise their structural integrity and safety. However, the detection of such internal damages is often challenging due to their concealed nature. Existing methods for detecting internal damage in timber structures suffer from low detection efficiency and recognition accuracy. This study proposes an end-to-end damage detection framework that integrates emphasized channel attention, propagation, and aggregation-time-delay neural network (ECAPA-TDNN) with percussion techniques. This framework is designed to evaluate the internal damage status of timber structures. The paper also introduces a method for creating internal voids within timber structures and fabricates timber specimens containing various sizes of internal damage. Vibration responses at different locations on the specimens are obtained using percussion techniques and serve as input data for the network. The results indicated that the ECAPA-TDNN architecture could effectively identifies damage within the timber structure and accurately distinguish between different sizes of damage, furnishing superlative recognition performance transcending prevailing benchmark models. This study elucidates the potential of percussion-based techniques and deep learning methods in the damage detection field, as well as the possibility for the proposed method to be extended to other applications, including transformer inspection, vehicle vibration monitoring, and medical treatment. [ABSTRACT FROM AUTHOR]

• We found the local high correlation mapping relationship between hazeclear image pairs. • A lightweight and fast network is proposed for real-time image dehazing. • A novel multi-scale feature extraction subnetwork is embedded into the dehazing model. • Our method surpasses previous state-of-the-art lightweight dehazing methods. • The size of our proposed network model is only 53.70 KB. • The average runtime for a 620 × 460 image on the CPU is as low as 0.12 seconds. [Display omitted] Haze causes visual degradation and obscures image information, which gravely affects the reliability of computer vision tasks in real-time systems. Leveraging an enormous number of learning parameters as the restoration costs, learning-based methods have gained significant success, but they are runtime intensive or memory inefficient. In this paper, we propose a local multi-scale feature aggregation network, called LMFA-Net, which has a lightweight model structure and can be used for real-time dehazing. By learning the local mapping relationship between the clean value of a haze image at a certain point and its surrounding local region, LMFA-Net can directly restore the final haze-free image. In particular, we adopt a novel multi-scale feature extraction sub-network (M-Net) to extract features from different scales. As a lightweight network, LMFA-Net can achieve fast and efficient dehazing. Extensive experiments demonstrate that our proposed LMFA-Net surpasses previous state-of-the-art lightweight dehazing methods in both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

• We are among the first ones to investigate the drawbacks of maxpooling in deep point cloud networks. And based on our insightful analysis, a kernel-based feature aggregation framework is innovatively proposed for orderless point analysis via deep networks. The framework enables modeling nonlinear feature relationships which are complementary to max-pooling in a flexible manner. • The order-invariance property of max-pooling can be naturally kept by simply choosing an order-invariant kernel function, e.g., the commonly used RBF kernel or Polynomial kernel. At the same time, the high efficiency of max-pooling is also maintained and only very minor additional computational cost is introduced, which will be demonstrated later through theoretical analysis and experimental study. • The superiority and generability of the proposed method have been extensively verified in both supervised and unsupervised point cloud analysis tasks with various representative backbone networks. Specifically, the supervised tasks include 3D object classification, part segmentation, scene segmentation while the unsupervised task refers to place retrieval. The backbone networks involved in the evaluation include PointNet, DGCNN and PCT. Various effective deep networks have been developed for analysis of 3D point clouds. One key step in these networks is to aggregate the features of orderless points into a compact representation for the cloud. As a typical order-invariant aggregation method, max-pooling has been widely applied. However, while enjoying simplicity and high efficiency, max-pooling does not fully exploit the feature information since it not only ignores the non-maximum elements in each feature dimension but also neglects the interactions between different dimensions. These drawbacks of max-pooling motivate us to explore advanced feature aggregation methods for 3D point cloud analysis. The desired advanced method should be capable of modeling richer information between the point features than max-pooling, and, at the same time, it can readily replace max-pooling without the need to modify other parts of the existing network architecture. To this end, this paper proposes a novel kernel-based feature aggregation framework for 3D point cloud analysis for the first time. The proposed method effectively considers all the elements in each dimension and models the nonlinear interactions between feature dimensions as complementary information to max-pooling. In addition, it is a plug-in module that can be integrated to many common networks as a replacement of max-pooling. Comprehensive experiments are conducted to demonstrate the consistently superior performance and high generality of the proposed method over max-pooling. Specifically, the proposed kernel-based feature aggregation framework consistently improves the max-pooling with three representative backbones of PointNet, DGCNN and PCT across four 3D point cloud based analysis tasks, including supervised 3D object classification, 3D part segmentation, indoor semantic segmentation and one additional unsupervised place retrieval task. Especially, it shows remarkable performance improvement over max-pooling in the unsupervised retrieval task, demonstrating its advantage in forming 3D point cloud representation. [ABSTRACT FROM AUTHOR]