4 accepted papers
Current few-shot action recognition methods achieve impressive performance by learning representative prototypes and designing diverse video matching strategies. However, these approaches typically face two critical limitations: i) prototypes learned through implicit sample interactions lack clear s
Low-light images are commonly present due to imaging factors such as insufficient light, night shooting and back lit. Existing low-light image enhancement (LLIE) methods typically rely on a low-light input image for enhancement, which seldom leverage information contained in its high-light counterpa…
Low-light image enhancement (LLIE) can be reformulated as an image-specific curve estimation (CE) problem. Traditional CE-based methods struggle with issues such as uniform processing across different regions, static parameter estimation, and lack of effective global semantic enhancement. To address…
The fusion of hyperspectral images (HSIs) and LiDAR data can improve land cover classification performance. However, existing fusion methods do not well consider the large discrepancies between two modalities (e.g., brightness, structure, and the possible misalignment), leading to limited improvemen…