Effectively capturing co-occurrence signals, such as hand shapes, facial expressions, and body postures, is critical for semantic understanding in sign language recognition (SLR) and translation (SLT). Although skeleton data offer greater efficiency and robustness than RGB inputs, existing methods typically rely on pairwise graph structures, limiting their ability to model complex high-order interactions across body regions. To address this limitation, we propose HyperSign, a hierarchical hypergraph neural network that systematically captures high-order co-occurrence patterns among diverse body parts. The Co-occurrence Graph Perception Module jointly learns relational structures via three complementary pathways: (1) traditional graph convolutions for modeling physical joint connections, (2) dynamic geometric hypergraphs constructed via k-nearest neighbors to encode local spatial patterns, and (3) soft hypergraphs generated by learnable prototypes to reveal latent semantic associations. To further enhance structural modeling and semantic consistency, a Meta-Part Hypergraph Fusion Module abstracts feature streams from the hands, face, and body into unified hypergraph nodes, while leveraging empirically derived co-occurrence priors to model high-order cross-part dependencies. Moreover, an uncertainty-aware collaborative distillation mechanism guides the model to focus on critical body regions. Extensive experiments on standard SLR and SLT benchmarks (e.g., PHOENIX-2014, PHOENIX-2014T, and CSL-Daily) demonstrate that HyperSign not only outperforms existing skeleton-based approaches in both speed and accuracy but also achieves competitive or superior results compared to several state-of-the-art RGB-based methods across multiple evaluation metrics.

Image deblurring aims to restore sharp images from degraded single-frame images, enabling industrial vision systems to operate reliably in complex industrial environments, such as high-speed and low-light conditions. Event cameras offer motion and edge information at high temporal resolution, allowing us to overcome the challenges red, green, and blue (RGB) cameras face with fast movements and lighting changes—common in industrial environments—by incorporating these cameras. However, considering the significant differences between different modalities, how to construct a multimodal vision system based on events and RGB images to jointly enhance the quality of multimodal features remains a topic worth exploring. In this article, we propose event-based deblurring network (EDNet), an event-based deblurring network that achieves efficient deblurring performance through cross-modal and cross-stage information interaction. We introduce the image-event interaction fusion module, which enhances the color information of images and the motion information of events through cross-modal attention, achieving higher quality feature fusion. To address the lack of low-level information during the image reconstruction stage, we designed the cross-stage information integration module, which effectively enhances the fidelity of image details and overall visual quality by integrating texture and fine-grained information from multiple feature layers. In addition, we provide an event-based deblurring test benchmark with authentic events and real blur in both indoor and outdoor scenes to evaluate the algorithm's performance and generalization capabilities. Experimental results demonstrate that EDNet outperforms other algorithms, restoring more detailed textures and exhibiting superior generalization capabilities, making it highly suitable for industrial applications.

Classical continuous sign language recognition (CSLR) suffers from some main challenges in real-world scenarios: accurate inter-frame movement trajectories may fail to be captured by traditional RGB cameras due to the motion blur, and valid information may be insufficient under low-illumination scenarios. In this paper, we for the first time leverage an event camera to overcome the above-mentioned challenges. Event cameras are bio-inspired vision sensors that could efficiently record high-speed sign language movements under low-illumination scenarios and capture human information while eliminating redundant background interference. To fully exploit the benefits of the event camera for CSLR, we propose a novel event-guided multi-modal CSLR framework, which could achieve significant performance under complex scenarios. Specifically, a time redundancy correction (TRCorr) module is proposed to rectify redundant information in the temporal sequences, directing the model to focus on distinctive features. A multi-modal cross-attention interaction (MCAI) module is proposed to facilitate information fusion between events and frame domains. Furthermore, we construct the first event-based CSLR dataset, namedEvCSLR, which will be released as the first event-based CSLR benchmark. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on EvCSLR and PHOENIX-2014 T datasets.