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.

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.

Underwater biological detection technology holds considerable promise for delving into the abundance of marine species and resources. It can seamlessly integrate into Autonomous Underwater Vehicles (AUVs), providing robust support for the application and enhancement of the Internet of Underwater Things (IoUT) systems. Recently, data-driven artificial intelligence technologies have exhibited considerable potential in underwater object detection. Nonetheless, the degraded underwater imaging environments present challenges for imaging devices on AUVs or other underwater devices. In this paper, we propose an advanced fish school detection framework designed to significantly contribute to the IoUT systems. This framework enhances performance in challenging conditions like inadequate illumination, blurriness, and high fish population density. The proposed framework demonstrates the capability to promptly identify changes in underwater species, such as migration or aggregation of large fish schools. The timely recognition of such changes offers vital information for IoUT applications, particularly in the management of marine biological resources. Furthermore, our framework can be flexibly deployed on AUVs and in-situ observation stations. Additionally, degraded underwater conditions and high acquisition costs hinder the scalability of data-driven methods. Therefore, we create a novel dense fish school detection dataset named DUFish, expertly annotated with high-quality bounding boxes. The proposed detection framework showcases exceptional performance on DUFish, outperforming state-of-the-art target detection algorithms. This has the potential to augment the capabilities of biological recognition and applications within the IoUT systems.

Underwater fine-grained classification technology is crucial for discerning subtle differences among marine life classes, playing a pivotal role in marine resource exploration and the discovery of new species. Autonomous underwater vehicles equipped with this technology can enhance their environmental interaction and perception, providing critical data for Internet of Underwater Things (IoUT) systems. However, popular vision transformer (ViT)-based methods encounter challenges in complex marine environments, particularly due to limited computational resources. In this article, we introduce an efficient ViT with token-selective and merging strategies (TSMVTs), which significantly improves underwater fine-grained classification performance while reducing the number of processed tokens. TSMVT can be flexibly integrated into various IoUT systems, promoting the discovery of new species and the sustainable development of marine ecology. First, we propose a dynamic token filtering mechanism that effectively retains important tokens, merges low-information tokens, and discards irrelevant background tokens, significantly reducing computational demands. Second, we propose the multihead attention weighting token-selective (MAWTS) module, which dynamically adjusts attention weights. MAWTS enables the network to focus on key features, such as fin shape, head structure, and body proportions, thereby improving classification accuracy. With a 30% reduction in tokens, TSMVT achieves superior precision in classifying marine species, enhancing its applicability on various underwater mobile platforms. Extensive experiments conducted on four marine and three terrestrial data sets demonstrate the outstanding accuracy and efficiency of the proposed TSMVT.

Images captured under challenging low-light conditions often suffer from myriad issues, including diminished contrast and obscured details, stemming from factors such as constrained lighting conditions or pervasive noise interference. Existing learning-based methods struggle in extreme low-light scenarios due to a lack of diverse paired datasets. In this letter, we meticulously curate a challenging real nighttime vision enhancement dataset called RNVE. RNVE comprises diverse data from various devices, including cameras and smartphones, available in both RGB and RAW formats. To enhance data diversity and enable comprehensive algorithm validation, we integrate synthetically generated low-light data, showcasing a spectrum of low-light effects. Additionally, we propose a low-light vision enhancement pipeline based on a dual-stream fusion network, proficiently improving the reconstruction quality of real nighttime scenes and restoring their authentic colors and contrast. Numerous experiments consistently demonstrate that the proposed pipeline excels in low-light enhancement and exhibits robust generalization capabilities across different datasets.