3 accepted papers
Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors th…
Contrastive clustering performs clustering and data representation in a unified model, where instance- and cluster-level constrastive learning are conducted simultaneously. However, commonly-used data augmentation methods make contrastive mechanism effect but may cause representation learning gettin…
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement. However, the commonly used hand-crafted priors and optimization-driven solutions lead to the absence of adaptivity and efficiency. To address these issues, in…