3 papers
cs.CV2026
EDCT-Bench: Uncovering Faithfulness Gaps in VLMs via Explanation-Driven Counterfactual Testing
Sihao Ding, Santosh Vasa, Aditi Ramadwar +1
Vision-Language Models (VLMs) can produce Natural Language Explanations (NLEs) that sound plausible yet remain inconsistent with the visual evidence they cite. We present Explanati…
cs.CV2025
Explanation-Driven Counterfactual Testing for Faithfulness in Vision-Language Model Explanations
Sihao Ding, Santosh Vasa, Aditi Ramadwar
Vision-Language Models (VLMs) often produce fluent Natural Language Explanations (NLEs) that sound convincing but may not reflect the causal factors driving predictions. This misma…
cs.CV2025
AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
Santosh Vasa, Aditi Ramadwar, Jnana Rama Krishna Darabattula +5
Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple…