Vision-language models achieve at most 61.9% accuracy on identifying image distortion types and severities, falling short of human majority-vote performance at 65.7%.
Learn to explain: Multimodal reasoning via thought chains for science question answering
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Empirical analysis identifies task-interfering layers in VLMs and proposes TaLo, a test-time method to bypass them for improved performance without training.
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DistortBench: Benchmarking Vision Language Models on Image Distortion Identification
Vision-language models achieve at most 61.9% accuracy on identifying image distortion types and severities, falling short of human majority-vote performance at 65.7%.
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Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models
Empirical analysis identifies task-interfering layers in VLMs and proposes TaLo, a test-time method to bypass them for improved performance without training.