11 papers · 1 filter
Prototype-Based Test-Time Adaptation of Vision-Language Models
Zhaohong Huang, Yuxin Zhang, Wenjing Liu +2
Test-time adaptation (TTA) has emerged as a promising paradigm for vision-language models (VLMs) to bridge the distribution gap between pre-training and test data. Recent works hav…
ID-Selection: Importance-Diversity Based Visual Token Selection for Efficient LVLM Inference
Zhaohong Huang, Wenjing Liu, Yuxin Zhang +2
Recent advances have explored visual token pruning to accelerate the inference of large vision-language models (LVLMs). However, existing methods often struggle to balance token im…
Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers
Yunshan Zhong, Yuyao Zhou, Yuxin Zhang +5
Data-free quantization (DFQ) enables model quantization without accessing real data, addressing concerns regarding data security and privacy. With the growing adoption of Vision Tr…
VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference
Pengfei Jiang, Hanjun Li, Linglan Zhao +4
In this study, we introduce a novel method called group-wise \textbf{VI}sual token \textbf{S}election and \textbf{A}ggregation (VISA) to address the issue of inefficient inference…
GS-Bias: Global-Spatial Bias Learner for Single-Image Test-Time Adaptation of Vision-Language Models
Zhaohong Huang, Yuxin Zhang, Jingjing Xie +2
Recent advances in test-time adaptation (TTA) for Vision-Language Models (VLMs) have garnered increasing attention, particularly through the use of multiple augmented views of a si…
TextDestroyer: A Training- and Annotation-Free Diffusion Method for Destroying Anomal Text from Images
Mengcheng Li, Fei Chao
In this paper, we propose TextDestroyer, the first training- and annotation-free method for scene text destruction using a pre-trained diffusion model. Existing scene text removal…