1 citations · 2 across the 8 of their papers we have counts for
4 papers · 2 filters
LoTLIP: Improving Language-Image Pre-training for Long Text Understanding
Wei Wu, Kecheng Zheng, Shuailei Ma +7
Understanding long text is of great demands in practice but beyond the reach of most language-image pre-training (LIP) models. In this work, we empirically confirm that the key rea…
MePT: Multi-Representation Guided Prompt Tuning for Vision-Language Model
Xinyang Wang, Yi Yang, Minfeng Zhu +3
Recent advancements in pre-trained Vision-Language Models (VLMs) have highlighted the significant potential of prompt tuning for adapting these models to a wide range of downstream…
Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
Shi Liu, Kecheng Zheng, Wei Chen
Existing Large Vision-Language Models (LVLMs) primarily align image features of vision encoder with Large Language Models (LLMs) to leverage their superior text generation capabili…
DreamLIP: Language-Image Pre-training with Long Captions
Kecheng Zheng, Yifei Zhang, Wei Wu +5
Language-image pre-training largely relies on how precisely and thoroughly a text describes its paired image. In practice, however, the contents of an image can be so rich that wel…