3 papers
cs.LG2026
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
cs.CV2026
Position: Reasoning After Perception Means Reasoning Without Vision
Hongcheng Gao, Zihao Huang, Jingyi Tang +12
A common belief in multimodal research is that the perceptual weaknesses of vision--language models can be compensated by stronger language reasoning (e.g., chain-of-thought, in-co…
cs.CV2026
Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items
Mengting Chen, Zhengrui Chen, Yongchao Du +16
Recent advances in image generation and editing have opened new opportunities for virtual try-on. However, existing methods still struggle to meet complex real-world demands. We pr…