6 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
TextRefiner: Internal Visual Feature as Efficient Refiner for Vision-Language Models Prompt Tuning
Jingjing Xie, Yuxin Zhang, Jun Peng +2
Despite the efficiency of prompt learning in transferring vision-language models (VLMs) to downstream tasks, existing methods mainly learn the prompts in a coarse-grained manner wh…
Advancing Multimodal Large Language Models with Quantization-Aware Scale Learning for Efficient Adaptation
Jingjing Xie, Yuxin Zhang, Mingbao Lin +2
This paper presents the first study to explore the potential of parameter quantization for multimodal large language models to alleviate the significant resource constraint encount…
UniPTS: A Unified Framework for Proficient Post-Training Sparsity
Jingjing Xie, Yuxin Zhang, Mingbao Lin +3
Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods, however, undergo significant perfo…
Distribution-Flexible Subset Quantization for Post-Quantizing Super-Resolution Networks
Yunshan Zhong, Mingbao Lin, Jingjing Xie +3
This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a post-training quantization method for super-resolution networks. Our motivation for developing DFSQ is bas…
Bi-directional Masks for Efficient N:M Sparse Training
Yuxin Zhang, Yiting Luo, Mingbao Lin +4
We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves…