Papers by Leonid Sigal
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. |
Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models (2025.findings-emnlp)
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Pushkar Shukla, Aditya Chinchure, Emily Diana, Alexander Tolbert, Kartik Hosanagar, Vineeth N. Balasubramanian, Leonid Sigal, Matthew A. Turk
| Challenge: | a new tool for analyzing and quantifying bias interactions in text-to-image models is being developed . a bias in text models can be deeply interrelated, but measuring such effects quantitatively remains a challenge. |
| Approach: | They propose a tool to quantify bias interactions in text-to-image models by analyzing and quantifying bias interactions along bias axes. |
| Outcome: | a new tool analyzes and quantifies bias interactions in text-to-image models . estimates show strong correlations with observed post-mitigation outcomes . |
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. |