Papers by Shih-Han Chou
MM-R3: On (In-)Consistency of Vision-Language Models (VLMs) (2025.findings-acl)
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| Challenge: | a flurry of research has been conducted on the performance of state-of-the-art (SoTA) Vision Language Models (VLMs) on a variety of tasks. |
| Approach: | They propose a benchmarking tool to analyze performance of SoTA Vision Language Models (VLMs) on three tasks: Question Rephrasing, Image Restyling, and Context Reasoning. |
| Outcome: | The proposed model achieves absolute improvements of 5.7% and 12.5% on widely used VLMs such as BLIP-2 and LLaVa 1.5M in terms of consistency over their existing counterparts. |