DIQ-H is a new benchmark for VLM performance under continuous adversarial image degradations, paired with VIR that raises annotation accuracy from 72.2% to 83.3%.
MMMU: A Massive Multi-discipline Multi- modal Understanding and Reasoning Benchmark for Expert AGI,
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Value-Guided Iterative Refinement and the DIQ-H Benchmark for Evaluating VLM Robustness
DIQ-H is a new benchmark for VLM performance under continuous adversarial image degradations, paired with VIR that raises annotation accuracy from 72.2% to 83.3%.