5 papers
Learnable Sparsity for Vision Generative Models
Yang Zhang, Er Jin, Wenzhong Liang +5
Diffusion models have achieved impressive advancements in various vision tasks. However, these gains often rely on increasing model size, which escalates computational complexity a…
Unconsciously Forget: Mitigating Memorization; Without Knowing What is being Memorized
Er Jin, Yang Zhang, Yongli Mou +4
Recent advances in generative models have demonstrated an exceptional ability to produce highly realistic images. However, previous studies show that generated images often resembl…
Segment Anything for Cell Tracking
Zhu Chen, Mert Edgü, Er Jin +1
Tracking cells and detecting mitotic events in time-lapse microscopy image sequences is a crucial task in biomedical research. However, it remains highly challenging due to dividin…
Minimalist Concept Erasure in Generative Models
Yang Zhang, Er Jin, Yanfei Dong +5
Recent advances in generative models have demonstrated remarkable capabilities in producing high-quality images, but their reliance on large-scale unlabeled data has raised signifi…
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction
Er Jin, Qihui Feng, Yongli Mou +4
Logical image understanding involves interpreting and reasoning about the relationships and consistency within an image's visual content. This capability is essential in applicatio…