Papers by Wan-Cyuan Fan
ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement (2025.emnlp-main)
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Ali Salamatian, Amirhossein Abaskohi, Wan-Cyuan Fan, Mir Rayat Imtiaz Hossain, Leonid Sigal, Giuseppe Carenini
| Challenge: | Chart question answering (CQA) is a key research challenge for large vision-language models . recent efforts focus on leveraging LVLMs directly on chart images . |
| Approach: | They propose a gaze-guided attention refinement that aligns image-text attention with human fixations to improve chart reasoning quality and interpretability. |
| Outcome: | The proposed approach improves answer accuracy and attention alignment yielding gains of up to 2.56 percentage points across multiple models. |
Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities (2025.acl-long)
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| Challenge: | Vision-language Models have been shown to be highly capable but lacking basic visual understanding skills. |
| Approach: | They propose to examine the limitations of vision-language models on visual tasks by constructing a series of tests that probe which components of design may be lacking. |
| Outcome: | The proposed tests compare VLMs to other models on visual encoders, intermediate vision-language projection and LLM-decoder outputs. |