September 2026

Mamba, a global context modeling paradigm with a selective scanning mechanism, has recently attracted increasing attention in multimodal image fusion. Multimodal fusion aims to preserve and enhance cr

Ground-based observations are often degraded by the combined effects of spatially non-uniform, strong stray-light contamination and noise. High-fidelity restoration of ground-based star images is a pr

Video Corpus Moment Retrieval (VCMR) is pivotal to multimodal understanding. However, existing methods rely heavily on large-scale annotated data, which limits their generalization and scalability. To

Image denoising is a long-standing inverse problem in computer vision. Despite recent progress, existing methods still struggle to balance precise feature focusing with structural fidelity under compl

Semantic correspondence seeks to establish matches between different instances of the same category. A common paradigm for this task leverages high-quality features from stable diffusion (SD) and DINO

Generating motion sequences conforming to a target style while adhering to the given content prompts requires accommodating both the content and style. In existing methods, the information usually onl

Multi-modal clustering aims to integrate complementary information from different modalities to uncover latent consistent structures and improve clustering performance. However, existing methods mainl

Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds components such as bou

Text screen content images (TSCIs) have been extensively applied in multimedia applications. When a TSCI is compressed by an encoder, the reconstructed image often exhibits changes of text stroke stru

Transformers have shown great promise in various point cloud comprehension tasks, but still face challenges due to the quadratic computational and memory cost when dealing with large-scale 3D point cl