3 papers
cs.CV2025
EvoVLMA: Evolutionary Vision-Language Model Adaptation
Kun Ding, Ying Wang, Shiming Xiang
Pre-trained Vision-Language Models (VLMs) have been exploited in various Computer Vision tasks (e.g., few-shot recognition) via model adaptation, such as prompt tuning and adapters…
cs.CV2024
A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem
Kun Ding, Ying Wang, Gaofeng Meng +1
The advent of pre-trained vision-language foundation models has revolutionized the field of zero/few-shot (i.e., low-shot) image recognition. The key challenge to address under the…
cs.CV2024
Calibrated Cache Model for Few-Shot Vision-Language Model Adaptation
Kun Ding, Qiang Yu, Haojian Zhang +2
Cache-based approaches stand out as both effective and efficient for adapting vision-language models (VLMs). Nonetheless, the existing cache model overlooks three crucial aspects.…