6 papers
FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing
Zihan Tang, Leqi Shen, Hui Chen +7
Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to encode dense documents incurs proh…
FastVID: Dynamic Density Pruning for Fast Video Large Language Models
Leqi Shen, Guoqiang Gong, Tao He +4
Video Large Language Models have demonstrated strong video understanding capabilities, yet their practical deployment is hindered by substantial inference costs caused by redundant…
DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval
Leqi Shen, Guoqiang Gong, Tianxiang Hao +6
The parameter-efficient adaptation of the image-text pretraining model CLIP for video-text retrieval is a prominent area of research. While CLIP is focused on image-level vision-la…
AdaTP: Attention-Debiased Token Pruning for Video Large Language Models
Fengyuan Sun, Leqi Shen, Hui Chen +3
Video Large Language Models (Video LLMs) have achieved remarkable results in video understanding tasks. However, they often suffer from heavy computational overhead due to the larg…
LLaVA-MLB: Mitigating and Leveraging Attention Bias for Training-Free Video LLMs
Leqi Shen, Tao He, Guoqiang Gong +5
Training-free video large language models (LLMs) leverage pretrained Image LLMs to process video content without the need for further training. A key challenge in such approaches i…
TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval
Leqi Shen, Tianxiang Hao, Tao He +5
Most text-video retrieval methods utilize the text-image pre-trained models like CLIP as a backbone. These methods process each sampled frame independently by the image encoder, re…