In this letter, we explore the detection of people with camouflage pattern in cluttered natural scenes. First, considering the lack of open evaluation and training data on camouflaged people detection, a specific dataset of camouflaged people in natural scenes is constructed by us for the first time to the best of our knowledge. Secon, due to the serious corruption of the discrimination of low-level features by the camouflage patterns and cluttered background, we extract the high-level semantic features in deep convolution network and introduce short connections in deconvolution phase, to construct the dense deconvolution network. In training procedure, we augment and shift the images of camouflaged people to generate the proper training data. Attributing to the usage and fusion of semantic information, the proposed network effectively labels the camouflaged people regions as a whole. Finally, we use the superpixel segmentation and spatial smoothness constraint for further improvement of the detection result. Experimental results demonstrate that the proposed method outperforms the classical camouflaged object detection method and typical CNN-based detection methods.

Deep neural networks have been proved vulnerable to being attacked by adversarial examples, which have attracted extensive attention from researchers. Existing GAN-based object detection adversarial example generation methods are efficient in generating speed but ignore the visual imperceptibility of adversarial examples. In this paper, to improve the visual imperceptibility of adversarial examples, we propose an object detection adversarial example generation method based on weakly perceptual perturbations in key regions. First, a positioning module based on the gradient-weighted activation mapping method is designed to analyze the key region of the object from the perspective of gradient propagation and use the key region in order to limit the range and amplitude of the perturbation. Second, the deep feature of the convolutional network is introduced to constrain the content of adversarial perturbation and improve the similarity between adversarial examples and original images. Finally, a postprocessing method based on median filtering is introduced to further correct the color deviation of adversarial examples and improve imperceptibility. The experimental results for VOC datasets show that the attack success rate increased by 4%, the PSNR increased by 8.6%, and the MSE and LPIPS decreased by 92.3% and 59.5%, respectively. It demonstrates that the proposed method can significantly improve the imperceptibility of the adversarial example with a high attack success rate.

In skeleton‐based action recognition, the graph convolutional network (GCN) has achieved great success. Modelling skeleton data in a suitable spatial‐temporal way and designing the adjacency matrix are crucial aspects for GCN‐based methods to capture joint relationships. In this study, we propose the spatial‐temporal slowfast graph convolutional network (STSF‐GCN) and design the adjacency matrices for the skeleton data graphs in STSF‐GCN. STSF‐GCN contains two pathways: (1) the fast pathway is in a high frame rate, and joints of adjacent frames are unified to build ‘small’ spatial‐temporal graphs. A new spatial‐temporal adjacency matrix is proposed for these ‘small’ spatial‐temporal graphs. Ablation studies verify the effectiveness of the proposed adjacency matrix. (2) The slow pathway is in a low frame rate, and joints from all frames are unified to build one ‘big’ spatial‐temporal graph. The adjacency matrix for the ‘big’ spatial‐temporal graph is obtained by computing self‐attention coefficients of each joint. Finally, outputs from two pathways are fused to predict the action category. STSF‐GCN can efficiently capture both long‐range and short‐range spatial‐temporal joint relationships. On three datasets for skeleton‐based action recognition, STSF‐GCN can achieve state‐of‐the‐art performance with much less computational cost.

Recently, polarization-based models for camouflaged object segmentation have attracted research attention. However, to construct this camouflaged object segmentation model, the main challenge is to effectively fuse polarization and light intensity features. Therefore, we propose a multi-modal camouflaged object segmentation method via gated fusion. First, the spatial positioning module is designed to perform channel calibration and global spatial attention alignment between polarization mode and light intensity mode from high-level feature representation to locate object positioning accurately. Then, the gated fusion module (GFM) is designed to selectively fuse the object information contained in the polarization and light intensity features. Among them, semantic information of location features is introduced in the GFM to guide each mode to aggregate dominant features. Finally, the features of each layer are aggregated to obtain an accurate segmentation result map. At the same time, considering the lack of public evaluation and training data on light intensity–polarization (I-P) camouflaged detection, we build the light I-P camouflaged detection dataset. Experimental results demonstrate that our proposed method outperforms other typical multi-modal segmentation methods in this dataset.