8 citations · 9 across the 6 of their papers we have counts for
4 papers · 1 filter
What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies
Duo Li, Haojun Pan, Zuhao Yang +4
Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating mismatched ones in a shared embed…
A Comprehensive Study on Visual Token Redundancy for Discrete Diffusion-based Multimodal Large Language Models
Duo Li, Zuhao Yang, Xiaoqin Zhang +2
Discrete diffusion-based multimodal large language models (dMLLMs) have emerged as a promising alternative to autoregressive MLLMs thanks to their advantages in parallel decoding a…
ToDRE: Effective Visual Token Pruning via Token Diversity and Task Relevance
Duo Li, Zuhao Yang, Xiaoqin Zhang +2
Visual token pruning, which aims to compress and prune redundant visual tokens, plays a critical role in efficient inference with large vision-language models (LVLMs). However, exi…
Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators
Yifan Pu, Zhuofan Xia, Jiayi Guo +9
This paper identifies significant redundancy in the query-key interactions within self-attention mechanisms of diffusion transformer models, particularly during the early stages of…