3 papers
cs.CV2026
MAIL++: Multi-Modal Bi-directional Agent Layer for Vision-Language Models
Kaixiang Chen, Pengfei Fang, Hui Xue
Adapting large vision-language models (VLMs) such as CLIP to downstream tasks remains challenging, as full fine-tuning is computationally prohibitive and prone to overfitting in lo…
cs.CV2026
TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
Xin Wang, Yixu Wang, Jiaming Zhang +6
Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to imperceptible adversarial perturbation…
cs.CV2025
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
Junbo Niu, Zheng Liu, Zhuangcheng Gu +58
We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational effi…