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Facial Attribute Classification (FAC) has attracted increasing attention in computer vision and pattern recognition. However, state-of-the-art FAC methods perform face detection/alignment and FAC independently. The inherent dependencies between these tasks are not fully exploited. In addition, most methods predict all facial attributes using the same CNN network architecture, which ignores the different learning complexities of facial attributes. To address the above problems, we propose a novel deep multi-task multi-label CNN, termed DMM-CNN, for effective FAC. Specifically, DMM-CNN jointly optimizes two closely-related tasks (i.e., facial landmark detection and FAC) to improve the performance of FAC by taking advantage of multi-task learning. To deal with the diverse learning complexities of facial attributes, we divide the attributes into two groups: objective attributes and subjective attributes. Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training. Furthermore, an adaptive thresholding strategy is developed to effectively alleviate the problem of class imbalance for multi-label learning. Experimental results on the challenging CelebA and LFWA datasets show the superiority of the proposed DMM-CNN method compared with several state-of-the-art FAC methods.

Recent methods in network pruning have indicated that a dense neural network involves a sparse subnetwork (called a winning ticket), which can achieve similar test accuracy to its dense counterpart with much fewer network parameters. Generally, these methods search for the winning tickets on well-labeled data. Unfortunately, in many real-world applications, the training data are unavoidably contaminated with noisy labels, thereby leading to performance deterioration of these methods. To address the above-mentioned problem, we propose a novel two-stream sample selection network (TS

Recently, graph-based methods have been widely applied to model fitting. However, in these methods, association information is invariably lost when data points and model hypotheses are mapped to the graph domain. In this paper, we propose a novel model fitting method based on co-clustering on bipartite graphs (CBG) to estimate multiple model instances in data contaminated with outliers and noise. Model fitting is reformulated as a bipartite graph partition behavior. Specifically, we use a bipartite graph reduction technique to eliminate some insignificant vertices (outliers and invalid model hypotheses), thereby improving the reliability of the constructed bipartite graph and reducing the computational complexity. We then use a co-clustering algorithm to learn a structured optimal bipartite graph with exact connected components for partitioning that can directly estimate the model instances (i.e., post-processing steps are not required). The proposed method fully utilizes the duality of data points and model hypotheses on bipartite graphs, leading to superior fitting performance. Exhaustive experiments show that the proposed CBG method performs favorably when compared with several state-of-the-art fitting methods.

Object tracking is a challenging task in computer vision based intelligent transportation systems. Recently, Siamese based object tracking methods have attracted significant attention due to their highly efficient performance. These tracking methods usually train a Siamese network to match the initial target patch of the first frame with candidates in a new frame. In these methods, the offline training of the deep neural network and the online instance searching are effectively combined. However, these methods usually do not include template update or object re-identification, which easily results in the drift problem. In this paper, we propose a novel real-time object tracking method to overcome the above problems by effectively combining a multi-stream Siamese network and a region-based convolutional neural network. Specifically, a novel multi-stream Siamese network is proposed to search the target and update the instance template in a new frame. In addition, a faster region-based convolutional neural network detector is used to perform object re-identification in order to improve the tracking performance by making full use of the object category information. These two networks are tightly coupled to ensure that the proposed tracking method has high efficiency and strong discriminative capability. Experimental results on several object tracking benchmarks show that our tracking method can effectively track vehicles and pedestrians in video sequences by exploiting the object category information. The proposed tracking method achieves real-time operations and outperforms several other state-of-the-art methods.

In recent years, deep learning-based person re-identification (Re-ID) methods have made significant progress. However, the performance of these methods substantially decreases when dealing with occlusion, which is ubiquitous in realistic scenarios. In this article, we propose a novel semantic-aware occlusion-robust network (SORN) that effectively exploits the intrinsic relationship between the tasks of person Re-ID and semantic segmentation for occluded person Re-ID. Specifically, the SORN is composed of three branches, including a local branch, a global branch, and a semantic branch. In particular, the local branch extracts part-based local features, and the global branch leverages a novel spatial-patch contrastive loss (SPC) to extract occlusion-robust global features. Meanwhile, the semantic branch generates a foreground-background mask for a pedestrian image, which indicates the non-occluded areas of the human body. The three branches are jointly trained in a unified multi-task learning network. Finally, pedestrian matching is performed based on the local features extracted from the non-occluded areas and the global features extracted from the whole pedestrian image. Extensive experimental results on a large-scale occluded person Re-ID dataset (i.e., Occluded-DukeMTMC) and two partial person Re-ID datasets (i.e., Partial-REID and Partial-iLIDS) show the superiority of the proposed method compared with several state-of-the-art methods for occluded and partial person Re-ID. We also demonstrate the effectiveness of the proposed method on two general person Re-ID datasets (i.e., Market-1501 and DukeMTMC-reID).

Deep Convolutional Neural Networks (DCNNs) have recently shown outstanding performance in semantic image segmentation. However, state-of-the-art DCNN-based semantic segmentation methods usually suffer from high computational complexity due to the use of complex network architectures. This greatly limits their applications in the real-world scenarios that require real-time processing. In this paper, we propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes, which achieves a good trade-off between accuracy and speed. Specifically, a Lightweight Baseline Network with Atrous convolution and Attention (LBN-AA) is firstly used as our baseline network to efficiently obtain dense feature maps. Then, the Distinctive Atrous Spatial Pyramid Pooling (DASPP), which exploits the different sizes of pooling operations to encode the rich and distinctive semantic information, is developed to detect objects at multiple scales. Meanwhile, a Spatial detail-Preserving Network (SPN) with shallow convolutional layers is designed to generate high-resolution feature maps preserving the detailed spatial information. Finally, a simple but practical Feature Fusion Network (FFN) is used to effectively combine both shallow and deep features from the semantic branch (DASPP) and the spatial branch (SPN), respectively. Extensive experimental results show that the proposed method respectively achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps on the challenging Cityscapes and CamVid test datasets (by only using a single NVIDIA TITAN X card). This demonstrates that the proposed method offers excellent performance at the real-time speed for semantic segmentation of urban street scenes.

Disparity estimation is a popular topic in computer vision and has drawn increasing attention in recent years. In this article, we propose a new multi-stage network for the purpose of two to three-dimensional video conversion that contains two training stages: an initial disparity estimation as the first training stage and depth-image-based rendering (DIBR) as an extra component to form the second training stage. In the first training stage, we propose a revised end-to-end feature pyramid stereo network, in which the original non-pyramid structure is replaced by a bottom-up convolutional neural network pyramid for disparity regression. It utilizes the spatial information by concatenating different scale features to boost the performance on boundary consistency. Mirror connections between feature extraction and disparity regression on the corresponding layers are also added to improve the quality of the results. In the second stage, we propose an improved disocclusion filling technique in the DIBR branch and connect the non-neural-network method to the disparity estimation network. This two-stage training strategy can work effectively to generate the improved disparity estimation for two to three-dimensional video conversion. Extensive experiments are conducted and some selected state-of-the-art algorithms are compared with our proposed approach on the popular KITTI2015 and Scene Flow datasets. The results demonstrate that our estimated disparity map can generate high quality 3D images.

In this paper, we propose a novel continuous latent semantic analysis fitting method, to efficiently and effectively estimate the parameters of model instances in data, based on latent semantic analysis and continuous preference analysis. Specifically, we construct a new latent semantic space (LSS): where inliers of different model instances are mapped into several independent directions, while gross outliers are distributed close to the origin of LSS. After that, we analyze the data distribution to effectively remove gross outliers in LSS, and propose an improved clustering algorithm to segment the remaining data points. On the one hand, the proposed fitting method is able to achieve excellent fitting results; due to the effective continuous preference analysis in LSS. On the other hand, the proposed method can efficiently obtain final fitting results due to the dimensionality reduction in LSS. Experimental results on both synthetic data and real images demonstrate that the proposed method achieves significant superiority over several state-of-the-art model fitting methods on both fitting accuracy and computational speed.

Existing learning-based dehazing methods are prone to cause excessive dehazing and failure to dense haze, mainly because that the global features of hazy images are not fully utilized, while the local features of hazy images are not enough discriminative. In this letter, we propose a Recurrent Context Aggregation Network (RCAN) to effectively dehaze images and restore color fidelity. In RCAN, an efficient and generic module, called Context Aggression Block (CAB), is designed to improve the feature representation by taking advantage of both global and local features, which are complementary for robust dehazing because that local features can capture different levels of haze, and global features can focus on textures and object edges of a whole image. In addition, RCAN adopts a deep recurrent mechanism to improve the dehazing performance without introducing additional network parameters. Extensive experimental results on both synthetic and real-world datasets show that the proposed RCAN performs better than other state-of-the-art dehazing methods.

Deep learning-based tracking methods have shown favorable performance on multiple benchmarks. However, most of these methods are not designed for real-time video surveillance systems due to the complex online optimization process. In this article, we propose a single-shot adversarial tracker (SAT) to efficiently locate objects of interest in surveillance videos. Specifically, we propose a lightweight convolutional neural network-based generator, which fuses multilayer feature maps to accurately generate the target probability map (TPM) for tracking. To more effectively train the generator, an adversarial learning framework is presented. During the online tracking stage, the learned TPM generator can be directly employed to generate the target probability map corresponding to the searching region in a single shot. The proposed SAT can lead to the average tracking speed of 212 FPS on a single GPU, while still achieving the favorable performance on several popular benchmarks. Furthermore, we also present a variant of SAT by considering both scale estimation and online updating in SAT, which achieves better accuracy than SAT while still maintaining very fast tracking speed (i.e., exceeding 100 FPS).

In this paper, a new robust model fitting method is proposed to efficiently segment multistructure data even when they are heavily contaminated by outliers. The proposed method is composed of three steps: first, a conventional greedy search strategy is employed to generate (initial) model hypotheses based on the sequential "fit-and-remove" procedure because of its computational efficiency. Second, to efficiently generate accurate model hypotheses close to the true models, a novel global greedy search strategy initially samples from the inliers of the obtained model hypotheses and samples subsequent data subsets from the whole input data. Third, mutual information theory is applied to fuse the model hypotheses of the same model instance. The conventional greedy search strategy is used to generate model hypotheses for the remaining model instances, if the number of retained model hypotheses is less than that of the true model instances after fusion. The second and the third steps are performed iteratively until an adequate solution is obtained. Experimental results demonstrate the effectiveness and efficiency of the proposed method for model fitting.

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps. DCFs have shown the ability of scale invariance, which is beneficial for face detection with high speed and promising performance. Therefore, extracting multi-scale features on an image pyramid employed in the conventional methods is not required in the proposed method, which can greatly improve its efficiency for face detection. Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed method for face detection., 11 figures, 30 pages, To appear in Neurocomputing

In this paper, we propose a two-step hypergraph reduction based fitting for unbalanced data. A hypergraph effectively characterizes the relationship between model hypotheses and data points for model fitting. However, a hypergraph-based fitting method often suffers from the problem of high computational cost due to the complex relationship between hyperedges and vertices. Hypergraph reduction algorithms are used to alleviate this problem, but they cannot work well for unbalanced data. To deal with the unbalanced model fitting problem, we first locally remove hyperedges corresponding to the same model instances in data, and then globally remove hyperedges corresponding to the bad model hypotheses. Moreover, we extend the binary incident matrix of a normal hypergraph to a continuous (soft) generalization, to improve the accuracy of hypergraph partition for model fitting. Experimental results on both synthetic data and real images demonstrate that the proposed method has significant superiority over several other state-of-the-art fitting methods.

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event cameras to object-level motion estimation or tracking is still in its infancy. The main idea behind this work is to propose a novel deep neural network to learn and regress a parametric object-level motion/transform model for event-based object tracking. To achieve this goal, we propose a synchronous Time-Surface with Linear Time Decay (TSLTD) representation, which effectively encodes the spatio-temporal information of asynchronous retinal events into TSLTD frames with clear motion patterns. We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression. Our method is compared with state-of-the-art object tracking methods, that are based on conventional cameras or event cameras. The experimental results show the superiority of our method in handling various challenging environments such as fast motion and low illumination conditions., 9 pages, 3 figures

Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods.

The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k.a. visual explanation, to interpret convolutional neural networks. Compared to iteration based saliency methods, single backward pass based saliency methods benefit from faster speed, and they are widely used in downstream visual tasks. Thus, we focus on single backward pass based methods. However, existing methods in this category struggle to uccessfully produce fine-grained saliency maps concentrating on specific target classes. That said, producing faithful saliency maps satisfying both target-selectiveness and fine-grainedness using a single backward pass is a challenging problem in the field. To mitigate this problem, we revisit the gradient flow inside the network, and find that the entangled semantics and original weights may disturb the propagation of target-relevant saliency. Inspired by those observations, we propose a novel visual saliency method, termed Target-Selective Gradient Backprop (TSGB), which leverages rectification operations to effectively emphasize target classes and further efficiently propagate the saliency to the image space, thereby generating target-selective and fine-grained saliency maps. The proposed TSGB consists of two components, namely, TSGB-Conv and TSGB-FC, which rectify the gradients for convolutional layers and fully-connected layers, respectively. Extensive qualitative and quantitative experiments on the ImageNet and Pascal VOC datasets show that the proposed method achieves more accurate and reliable results than the other competitive methods. Code is available at https://github.com/123fxdx/CNNvisualizationTSGB., Comment: Accepted by IEEE Transactions on Image Processing. Index Terms: Model interpretability, explanation, saliency map, CNN visualization

Real-time semantic segmentation, which aims to achieve high segmentation accuracy at real-time inference speed, has received substantial attention over the past few years. However, many state-of-the-art real-time semantic segmentation methods tend to sacrifice some spatial details or contextual information for fast inference, thus leading to degradation in segmentation quality. In this paper, we propose a novel Deep Multi-branch Aggregation Network (called DMA-Net) based on the encoder-decoder structure to perform real-time semantic segmentation in street scenes. Specifically, we first adopt ResNet-18 as the encoder to efficiently generate various levels of feature maps from different stages of convolutions. Then, we develop a Multi-branch Aggregation Network (MAN) as the decoder to effectively aggregate different levels of feature maps and capture the multi-scale information. In MAN, a lattice enhanced residual block is designed to enhance feature representations of the network by taking advantage of the lattice structure. Meanwhile, a feature transformation block is introduced to explicitly transform the feature map from the neighboring branch before feature aggregation. Moreover, a global context block is used to exploit the global contextual information. These key components are tightly combined and jointly optimized in a unified network. Extensive experimental results on the challenging Cityscapes and CamVid datasets demonstrate that our proposed DMA-Net respectively obtains 77.0% and 73.6% mean Intersection over Union (mIoU) at the inference speed of 46.7 FPS and 119.8 FPS by only using a single NVIDIA GTX 1080Ti GPU. This shows that DMA-Net provides a good tradeoff between segmentation quality and speed for semantic segmentation in street scenes.

Visible-infrared person re-identification (VI-ReID) aims to search identities of pedestrians across different spectra. In this task, one of the major challenges is the modality discrepancy between the visible (VIS) and infrared (IR) images. Some state-of-the-art methods try to design complex networks or generative methods to mitigate the modality discrepancy while ignoring the highly non-linear relationship between the two modalities of VIS and IR. In this paper, we propose a non-linear middle modality generator (MMG), which helps to reduce the modality discrepancy. Our MMG can effectively project VIS and IR images into a unified middle modality image (UMMI) space to generate middle-modality (M-modality) images. The generated M-modality images and the original images are fed into the backbone network to reduce the modality discrepancy.Furthermore, in order to pull together the two types of M-modality images generated from the VIS and IR images in the UMMI space, we propose a distribution consistency loss (DCL) to make the modality distribution of the generated M-modalities images as consistent as possible. Finally, we propose a middle modality network (MMN) to further enhance the discrimination and richness of features in an explicit manner. Extensive experiments have been conducted to validate the superiority of MMN for VI-ReID over some state-of-the-art methods on two challenging datasets. The gain of MMN is more than 11.1% and 8.4% in terms of Rank-1 and mAP, respectively, even compared with the latest state-of-the-art methods on the SYSU-MM01 dataset.

One of the main challenges in facial expression recognition (FER) is to address the disturbance caused by various disturbing factors, including common ones (such as identity, pose, and illumination) and potential ones (such as hairstyle, accessory, and occlusion). Recently, a number of FER methods have been developed to explicitly or implicitly alleviate the disturbance involved in facial images. However, these methods either consider only a few common disturbing factors or neglect the prior information of these disturbing factors, thus resulting in inferior recognition performance. In this paper, we propose a novel Dual-branch Disturbance Disentangling Network (D3Net), mainly consisting of an expression branch and a disturbance branch, to perform effective FER. In the disturbance branch, a label-aware sub-branch (LAS) and a label-free sub-branch (LFS) are elaborately designed to cope with different types of disturbing factors. On the one hand, LAS explicitly captures the disturbance due to some common disturbing factors by transfer learning on a pretrained model. On the other hand, LFS implicitly encodes the information of potential disturbing factors in an unsupervised manner. In particular, we introduce an Indian buffet process (IBP) prior to model the distribution of potential disturbing factors in LFS. Moreover, we leverage adversarial training to increase the differences between disturbance features and expression features, thereby enhancing the disentanglement of disturbing factors. By disentangling the disturbance from facial images, we are able to extract discriminative expression features. Extensive experiments demonstrate that our proposed method performs favorably against several state-of-the-art FER methods on both in-the-lab and in-the-wild databases.

Few-shot action recognition has drawn growing attention as it can recognize novel action classes by using only a few labeled samples. In this paper, we propose a novel semantic-guided relation propagation network (SRPN), which leverages semantic information together with visual information for few-shot action recognition. Different from most previous works that neglect semantic information in the labeled data, our SRPN directly utilizes the semantic label as an additional supervisory signal to improve the generalization ability of the network. Besides, we treat the relation of each visual-semantic pair as a relational node, and we use a graph convolutional network to model and propagate such sample relations across visual-semantic pairs, including both intra-class commonality and inter-class uniqueness, to guide the relation propagation in the graph. However, since videos contain crucial sequences and ordering information, we propose a novel spatial-temporal difference module, which can facilitate the network to enhance the visual feature learning ability at both feature level and granular level for videos. Extensive experiments conducted on several challenging benchmarks demonstrate that our SRPN outperforms several state-of-the-art methods with a significant margin.