4 accepted papers
"While personalized text-to-image generation has enabled the learning of a single concept from multiple images, a more practical yet challenging scenario involves learning multiple concepts within a single image. However, existing works tackling this scenario heavily rely on extensive human annotati…
"We tackle the problem of Continual Category Discovery (CCD), which aims to automatically discover novel categories in a continuous stream of unlabeled data while mitigating the challenge of catastrophic forgetting – an open problem that persists even in conventional, fully supervised continual lear…
"Point-drag-based image editing methods, like DragDiffusion, have attracted significant attention. However, point-drag-based approaches suffer from computational overhead and misinterpretation of user intentions, due to the sparsity of point-based editing instructions. In this paper, we propose a re…
"Token compression expedites the training and inference of Vision Transformers (ViTs) by reducing the number of the redundant tokens, , pruning inattentive tokens or merging similar tokens. However, when applied to downstream tasks, these approaches suffer from significant performance drop when the…