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cs.CV2025
AutoRefiner: Improving Autoregressive Video Diffusion Models via Reflective Refinement Over the Stochastic Sampling Path
Zhengyang Yu, Akio Hayakawa, Masato Ishii +4
Autoregressive video diffusion models (AR-VDMs) show strong promise as scalable alternatives to bidirectional VDMs, enabling real-time and interactive applications. Yet there remai…
cs.CV2025
Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation
Pengchen Liang, Haishan Huang, Bin Pu +7
Large-scale pre-trained models, such as Vision Foundation Models (VFMs), have demonstrated impressive performance across various downstream tasks by transferring generalized knowle…
cs.CV2025★ 1 cited
Semantically Guided Dynamic Visual Prototype Refinement for Compositional Zero-Shot Learning
Zhong Peng, Yishi Xu, Gerong Wang +4
Compositional Zero-Shot Learning (CZSL) seeks to recognize unseen state-object pairs by recombining primitives learned from seen compositions. Despite recent progress with vision-l…