FiVL augments vision-language instruction data with GPT-4o-extracted key expressions and segmentation masks, trains LLaVA with a vision-modeling loss that predicts vocabulary tokens for image patches, and measures visual reliance via accuracy drop when key regions are masked.
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA
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abstract
We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. In contrast to existing VQA test sets, CARETS features balanced question generation to create pairs of instances to test models, with each pair focusing on a specific capability such as rephrasing, logical symmetry or image obfuscation. We evaluate six modern VQA systems on CARETS and identify several actionable weaknesses in model comprehension, especially with concepts such as negation, disjunction, or hypernym invariance. Interestingly, even the most sophisticated models are sensitive to aspects such as swapping the order of terms in a conjunction or varying the number of answer choices mentioned in the question. We release CARETS to be used as an extensible tool for evaluating multi-modal model robustness.
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cs.CV 1years
2024 1verdicts
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FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
FiVL augments vision-language instruction data with GPT-4o-extracted key expressions and segmentation masks, trains LLaVA with a vision-modeling loss that predicts vocabulary tokens for image patches, and measures visual reliance via accuracy drop when key regions are masked.