TraversalBench shows self-intersections cause the sharpest performance drops for VLMs on exact path traversal, with errors localized at the first crossing.
Are vision language models texture or shape biased and can we steer them?
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AVA-Bench evaluates vision foundation models by disentangling 14 atomic visual abilities with aligned training-test distributions to reveal precise ability fingerprints.
citing papers explorer
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TraversalBench: Challenging Paths to Follow for Vision Language Models
TraversalBench shows self-intersections cause the sharpest performance drops for VLMs on exact path traversal, with errors localized at the first crossing.
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AVA-Bench: Atomic Visual Ability Benchmark for Vision Foundation Models
AVA-Bench evaluates vision foundation models by disentangling 14 atomic visual abilities with aligned training-test distributions to reveal precise ability fingerprints.