4 papers
Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
Xuehui Wang, Xuankun Yang, Wei Shen
Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instr…
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
Zhe Chen, Weiyun Wang, Yue Cao +39
We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing signif…
Generalized Tensor-based Parameter-Efficient Fine-Tuning via Lie Group Transformations
Chongjie Si, Zhiyi Shi, Xuehui Wang +3
Adapting pre-trained foundation models for diverse downstream tasks is a core practice in artificial intelligence. However, the wide range of tasks and high computational costs mak…
Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning
Chongjie Si, Xuehui Wang, Xue Yang +6
Adapting pre-trained foundation models for various downstream tasks has been prevalent in artificial intelligence. Due to the vast number of tasks and high costs, adjusting all par…