Graph convolutional networks (GCNs) have achieved outstanding performances on skeletonbased action recognition. However, several problems remain in existing GCN-based methods, and the spatialtemporal features are not discriminative enough. Temporal convolution with one fixed kernel cannot obtain more discriminative temporal features for different actions. Besides, only a single-scale feature is used for classification, which ignores the multilevel information. In this paper, we propose a novel multi-scale and multi-stream improved graph convolutional network (MM-IGCN). In each spatial-temporal block of MMIGCN, we employ an improved temporal convolution with multiple parallel kernels to enhance the temporal features. An improved GCN and an enhanced attention module are adopted in the block to strengthen spatialtemporal features. A multi-scale structure is first introduced in action recognition to obtain the multilevel information. The improved spatial-temporal blocks and multi-scale structure compose our single-stream model. Moreover, we adopt the bone cosine distance as a novel input feature. Five streams (joint, bone, their motions, and bone cosine distance) of features are fed into our single-stream model respectively, which compose our MM-IGCN. Experiments on two large datasets, NTU-RGB+D and NTU-RGB+D-120, illustrate that our single-stream model achieves state-of-the-art, and our MM-IGCN is far superior to other models.

Given a video containing a person, the video-based person re-identification (Re-ID) task aims to identify the same person from videos captured under different cameras. How to embed spatial-temporal information of a video into its feature representation is a crucial challenge. Most existing methods have failed to make full use of the relationship between frames during feature extraction. In this work, we propose a plug-and-play non-local attention module (NLAM) for frame-level feature extraction. NLAM, based on global spatial attention and channel attention, helps the network to determine the location of the person in each frame. Besides, we propose a non-local temporal pooling (NLTP) method used for temporal features’ aggregation, which can effectively capture long-range and global dependencies among the frames of the video. Our model obtained impressive results on different datasets compared to the state-of-the-art methods. In particular, it achieved the rank-1 accuracy of 86.3% on the MARS (Motion Analysis and Re-identification Set) dataset without re-ranking, which is 1.4% higher than the state-of-the-art way. On the DukeMTMC-VideoReID (Duke Multi-Target Multi-Camera Video Reidentification) dataset, our method also had an excellent performance of 95% rank-1 accuracy and 94.5% mAP (mean Average Precision).

Colorectal cancer (CRC) remains one of the most prevalent and life-threatening diseases globally. In the development of computer-aided diagnosis systems for CRC, accurate polyp segmentation plays a pivotal role. Despite technological advancements, the segmentation of diminutive and multiple polyps continues to remain significant challenges. To address these issues, we introduce an auxiliary attention module (AAM), which leverages a channel attention module, a spatial attention module, and a feature enhancement module to simultaneously capture both global and local features. This parallel learning approach enhances the ability of model to focus on global details and localized regions, particularly for small and flat polyps. Additionally, we propose an area adaptive loss (AAL) to mitigate the under-segmentation of diminutive and multiple polyps. The AAL dynamically adjusts loss weights based on the size and quantity of polyps, prioritizing smaller and more hard samples during training. This adaptive mechanism ensures that the model is better equipped to handle imbalanced and difficult cases, thereby improving segmentation accuracy. Our proposed framework, DoubleAANet, integrates the AAM and AAL to emphasize difficult examples and localized information, enhancing the robustness and precision. To validate the effectiveness and generalization capability of our approach, we conduct extensive experiments on three diverse datasets—Kvasir-SEG, CVC-ClinicDB, and Kvasir-Sessile—as well as a cross-dataset evaluation. The results demonstrate state-of-the-art performance, with our method achieving 86.65% mIoU and 92.17% mDSC on Kvasir-SEG, 90.00% mIoU and 94.58% mDSC on CVC-ClinicDB, and 77.22% mIoU and 82.39% mDSC on Kvasir-Sessile. Notably, on the Kvasir-Sessile dataset, which contains small, flat, and diminutive polyps, our approach achieves a 12.17% improvement in recall, effectively addressing the issue of missed polyps while maintaining a superior balance between recall and precision. The cross-dataset evaluation further underscores the generalization ability of DoubleAANet, achieving 76.90% mIoU and 84.77% mDSC. Comprehensive experimental results confirm that our proposed method outperforms most existing state-of-the-art segmentation techniques, offering a significant advancement in polyp segmentation for CRC diagnosis. Source code will be available at https://github.com/feixiangdu/DoubleAANet.

The degradation and failure of the urban water supply network may lead to serious safety hazards, including pipe breaks, water supply interruptions, water resource losses, and contaminant intrusions. The risk evaluation of water supply pipeline failure in a distribution network is a challenging task, because most of the available data cannot fully reflect pipeline failure events and many of the mechanisms still cannot be fully understood. Therefore, a predictive model is urgently needed to assess pipeline failure risk based on available data. In this paper, based on the traditional risk assessment theory, seven main factors affecting pipeline failure are selected and scored, and then a pipeline failure model is established by using the particle swarm optimization (PSO) neural network. The model uses the neural network training of historical data to evaluate the failure of the water supply pipeline, and the PSO is used to optimize the neural network to effectively improve the training time and accuracy. The model error and correlation coefficient are 0.003 and 0.987, respectively. The proposed model can be used as a powerful support tool to assist infrastructure managers and pipeline maintainers in their plans and decision-making.

In the past decade, convolutional neural networks (CNNs) and transformers have achieved wide application in semantic segmentation tasks. Although CNNs with transformer models greatly improve performance, the global context modeling remains inadequate. Recently, Mamba achieved great potential in vision tasks, showing its advantages in modeling long-range dependency. In this article, we propose a lightweight efficient CNN-Mamba network for semantic segmentation, dubbed as ECMNet. ECMNet combines CNN with Mamba skillfully in a capsule-based framework to address their complementary weaknesses. Specifically, we design an enhanced dual-attention block for a lightweight bottleneck. In order to improve the representation ability of the feature, we devise a multi-scale attention unit to integrate multi-scale feature aggregation, spatial aggregation, and channel aggregation. Moreover, a Mamba enhanced feature fusion module merges diverse level feature, significantly enhancing segmented accuracy. Extensive experiments on two representative datasets demonstrate that the proposed model excels in accuracy and efficiency balance, achieving 70.6% mIoU on Cityscapes and 73.6% mIoU on CamVid test datasets, with 0.87 M parameters and 8.27G FLOPs on a single RTX 3090 GPU platform. Source code will be available at https://github.com/feixiangdu/ECMNet .