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cs.CV2024
Enhancing Descriptive Captions with Visual Attributes for Multimodal Perception
Yanpeng Sun, Jing Hao, Ke Zhu +6
Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods for generating such captions often rely on distill…
cs.LG2024
Continual SFT Matches Multimodal RLHF with Negative Supervision
Ke Zhu, Yu Wang, Yanpeng Sun +4
Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiori…
cs.CV2024
Self-Supervised Visual Preference Alignment
Ke Zhu, Zheng Ge, Liang Zhao +1
This paper makes the first attempt towards unsupervised preference alignment in Vision-Language Models (VLMs). We generate chosen and rejected responses with regard to the original…