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Remote sensing image (RSI) target detection methods based on traditional multiscale feature fusion (MSFF) have achieved great success. However, the traditional MSFF method significantly increases the computational cost during model training and inference, and the simple fusion operation may lead to the semantic confusion of the feature map, which cannot realize the refined extraction of features by the model. In order to reduce the computational effort associated with the MSFF operation and to enable the features in the feature map to present an accurate, fine-grained distribution, we propose a single-stage detection model (RS-YOLO). Our main additions to RS-YOLO are a computationally smaller and faster Quick and Small E-ELEN (QS-E-ELEN) module and a feature refinement extraction (FRE) module. In the QS-E-ELEN module, We utilize QSBlock, jump-join, and convolution operations to fuse features on different scales and reduce the computational effort of the model by exploiting the similarity of the RSI feature map channels. In order for the model to better utilize the enhanced features, we designed the FRE module to make the location of the enhanced features more accurate and fine. By conducting experiments on the popular NWPU-VHR- 10 and SSDD datasets, we derive results showing that RS-YOLO outperforms most mainstream models in terms of the tradeoff between accuracy and speed. Specifically, in terms of accuracy, it improves 1.6$\%$ and 1.7$\%$ compared to the current state-of-the-art models, respectively. At the same time, RS-YOLO reduces the number of parameters and computational effort.

RGB-D salient object detection (RGB-D SOD) has currently attracted much attention for its prospect of broad application. On the basis of the "encoder-decoder" paradigm of the fully convolutional network (FCN), many FCN-based strategies have emerged and achieved huge progress, but underestimated the potential of level-specific characteristics of multi-modal features. In this paper, we propose the adaptive guided fusion network (AGFNet) to further mine the potential information between the depth image and the RGB image, and design an adaptive fusion and coarse-to-fine decoding strategy to achieve high-precision detection of salient objects. Specifically, we first use a two-stream encoder to extract the multi-level features of the RGB image and depth image but refrain from the previous practice of using depth features for each layer. Second, a simple but effective way named multi-modal selective fusion strategy is designed to fuse the multi-level features. Third, for enhancement of contextual information of each level adaptively, an adaptive cross fusion module (ACFM) fuses the features at all levels and outputs a coarse saliency map. Finally, a guided attention refinement module (GARM) utilizes the coarse saliency map to guide the final features from ACFM to realize the enhancement and obtain a refined saliency map. Our method is compared with other state-of-the-art RGB-SOD methods through extensive experiments, and the results demonstrate the superiority of our proposed AGFNet. The source code of this project is available at https://github.com/HaodongSun809/my_AGFNet.git. [ABSTRACT FROM AUTHOR]

In modern energy systems, substations are the core of electricity transmission and distribution. However, similar appearance and small size pose significant challenges for automatic identification of electrical devices. To address these issues, we collect and annotate the substation rotated device dataset (SRDD). Further, feature fusion and feature refinement network (F3RNet) are constructed based on the classic structure pattern of backbone-neck-head. Considering the similar appearance of electrical devices, the deconvolution fusion module (DFM) is designed to enhance the expression of feature information. The balanced feature pyramid (BFP) is embedded to aggregate the global feature. The feature refinement is constructed to adjust the original feature maps by considering the feature alignment between the anchors and devices. It can generate more accurate feature vectors. To address the problem of sample imbalance between electrical devices, the gradient harmonized mechanism (GHM) loss is utilized to adjust the weight of each sample. The ablation experiments are conducted on the SRDD dataset. F3RNet achieves the best detection performance compared with classical object detection networks. Also, it is verified that the features from global feature maps can effectively recognize the similar and small devices.

Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images. [ABSTRACT FROM AUTHOR]

Discriminative correlation filters (DCF) have achieved much success in visual tracking. However, most of them simply rely on the features extracted by the last layer of the backbone, while ignoring the low-level rich structural information. In addition, they normally minimize the tailored objective functions to predict the target model in a direct way, which introduces inductive bias and limits the expressivity of the trackers. In view of this, a pyramidal feature fusion module is proposed in this paper to integrate the low-resolution, semantically strong features with high-resolution, semantically weak features. Then, an asymmetric Transformer structure is applied to predict the weights of the model. Finally, a feature refinement module is employed to optimize the search features. Extensive experiments on 5 mainstream datasets demonstrate the superiority of our tracker, where it has achieved better feature expression and more precise target localization. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images.

• DPT extracts deep, specific, and global features from video frames. • Instead of considering a single layer, features are extracted from multi-scales. • PRB is designed that boost the frames representations before passing to CH. • Progressive features fusion ensures features refinement before predictions. Video data are witnessing exponential growth, and extracting summarized information is challenging. It is always necessary to reduce the load of video traffic for the efficient video storage, transmission, and retrieval requirements. The aim of video summarization (VS) is to extract the most important contents from video repositories effectively. Recent attempts have used fewer representative features, which have been fed to recurrent networks to achieve VS. However, generating the desired summaries can become challenging due to the limited representativeness of extracted features and a lack of consideration for feature refinement. In this article, we introduce a vision transformer (ViT)-assisted deep pyramidal refinement network that can extract and refine multi-scale features and can predict an importance score for each frame. The proposed network comprises four main modules; initially, a dense prediction transformer with a ViT backbone is applied for the first time in this domain to acquire the optimal representations from the input frames. Then, feature maps are obtained from various layers separately and processed individually to support multi-scale progressive feature fusion and refinement before the data are passed to the ultimate prediction module. Next, a customized pyramidal refinement block is employed to refine the multi-level feature set before predicting the importance scores. Finally, video summaries are produced by selecting keyframes based on the predictions. To explore the performance of the proposed network, extensive experiments are conducted on the TVSum and SumMe datasets, and our network is found to achieve F1-scores of 62.4% and 51.9%, respectively, outperforming state-of-the-art alternatives by 0.9% and 0.5%. [ABSTRACT FROM AUTHOR]