4 citations · 6 across the 6 of their papers we have counts for
6 papers
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
Zeyi Sun, Ziyang Chu, Pan Zhang +6
In-context generation is a key component of large language models' (LLMs) open-task generalization capability. By leveraging a few examples as context, LLMs can perform both in-dom…
MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models
Ziyu Liu, Yuhang Zang, Xiaoyi Dong +7
Visual preference alignment involves training Large Vision-Language Models (LVLMs) to predict human preferences between visual inputs. This is typically achieved by using labeled d…
MMDU: A Multi-Turn Multi-Image Dialog Understanding Benchmark and Instruction-Tuning Dataset for LVLMs
Ziyu Liu, Tao Chu, Yuhang Zang +8
Generating natural and meaningful responses to communicate with multi-modal human inputs is a fundamental capability of Large Vision-Language Models(LVLMs). While current open-sour…
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data
Zeyi Sun, Tong Wu, Pan Zhang +5
Recent years have witnessed remarkable progress in multi-view diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-followi…
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
Ziyu Liu, Zeyi Sun, Yuhang Zang +6
CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of candidates, yet its focus on broad asso…
Alpha-CLIP: A CLIP Model Focusing on Wherever You Want
Zeyi Sun, Ye Fang, Tong Wu +6
Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks. It aligns textual and visual mod…