VISTAQA is a new benchmark for joint visual question answering correctness and pixel-level grounding, evaluated with the GROVE metric that uses per-sample geometric mean to require both dimensions to succeed.
MathVista: Evaluating mathematical reasoning of foundation models in visual contexts
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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CAVE is a GRPO-based process-reward method that improves VLMs on fragmented visual reasoning by crediting intermediate actions via belief update, evidence acquisition, and adaptive focus, shown on TRACER-Bench and public benchmarks.
citing papers explorer
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VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence
VISTAQA is a new benchmark for joint visual question answering correctness and pixel-level grounding, evaluated with the GROVE metric that uses per-sample geometric mean to require both dimensions to succeed.
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CAVE: A Structured Credit Assignment Approach for Fragmented Visual Evidence Reasoning
CAVE is a GRPO-based process-reward method that improves VLMs on fragmented visual reasoning by crediting intermediate actions via belief update, evidence acquisition, and adaptive focus, shown on TRACER-Bench and public benchmarks.