Image dehazing is an important task since it is the prerequisite for many downstream high-level computer vision tasks. Previous dehazing methods depend on either the hand-designed priors/assumptions or plenty of paired hazy-clear images for supervised learning, which are not easy to implement in practice. At the same time, synthesizing hazy images is also significant in many scenes like network training and multi-weather image generation. Current models cannot hazing and dehazing simultaneously, which burdens the task since each direction of mapping needs one time of training. In this paper, we change the viewpoint of this task to image translation and develop a weakly supervised framework to achieve it. Instead of simply considering the hazy image as the source domain and the haze-free image as the target domain for translation, we design a feature representation scheme that generates a domain indicator, and embed it into the decoder to achieve both hazing and dehazing within one network. This design significantly reduces the complexity of network and can be more easily extended to multi-domain translation tasks than the previous methods, which need one pair of generator-discriminator for each direction of the translation. Meanwhile, aiming at solving the haze-relevant task, we design a haze attention module, which takes the local entropy map as the input. Unlike the previous weakly supervised dehazing methods, our approach only requires unpaired hazy and haze-free images rather than any intermediate supervising data like the transmission map or atmospheric light defined in the atmospheric scattering model. Experimental results on synthetic datasets show our method can achieve competitive results when compared with the state-of-the-art methods and yield more appealing dehazing and hazing results on real-world images.

While sign language combines sequential hand motions with concurrent non-manual cues (e.g., mouth shapes and head tilts), current recognition systems lack multimodal annotation methods capable of capturing their hierarchical semantics. To bridge this gap, we propose GSP-MC, the first method integrating generative large language models into sign language recognition. It leverages retrieval-augmented generation with domain-specific large language models and expert-validated corpora to produce precise multipart descriptions. A dual-encoder architecture bidirectionally aligns hierarchical skeleton features with multi-level text descriptions (global, synonym, part) through probabilistic matching. The approach combines global and part-level losses with KL divergence optimization, ensuring robust alignment across relevant text-skeleton pairs while capturing sign semantics and detailed dynamics. Experiments demonstrate state-of-the-art performance, achieving 97.1% accuracy on the Chinese SLR500 (surpassing SSRL’s 96.9%) and 97.07% on the Turkish AUTSL (exceeding SML’s 96.85%), confirming cross-lingual potential for inclusive communication technologies.

Gesture recognition plays a crucial role in natural human-computer interaction and sign language recognition. Despite considerable progress in normal daylight, research dedicated to gesture recognition in dark environments is scarce. This is partly due to the lack of sufficient datasets for such a task. We bridge the gap of the lack of data for this task by collecting a new dataset: a large-scale multimodal video dataset for gesture recognition in darkness (MGR-Dark). MGR-Dark is distinguished from existing gesture datasets by its gesture collection in darkness, multimodal videos(RGB, Depth, and Infrared), and high video quality. To the best of our knowledge, this is the first multimodal dataset dedicated to human gesture action in dark videos of high quality. Building upon this, we propose a Modality Translation and Cross-modal Distillation (MTCD) RGB-IR benchmark framework. Specifically, the modality translator is firstly utilized to transfer RGB data to pseudo-Infrared data, a progressive cross-modal feature distillation module is then designed to exploit the underlying relations between RGB, pseudo-Infrared and Infrared modalities to guide RGB feature learning. The experiments demonstrate that the dataset and benchmark proposed in this paper are expected to advance research in gesture recognition in dark videos. Our dataset and code can be found at https://github.com/Grass-Shi/MGR-DarkMGR-Dark.