SpecVQA is a new benchmark dataset and evaluation suite for testing multimodal large language models on scientific spectral image understanding and visual question answering, supported by a curve-preserving sampling method that improves results.
International journal of computer vision40(2), 99–121 (2000)
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Wasserstein equilibrium decoding that improves accuracy and convergence speed for small VLMs on medical VQA benchmarks by using semantic consensus instead of lexical order.
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SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images
SpecVQA is a new benchmark dataset and evaluation suite for testing multimodal large language models on scientific spectral image understanding and visual question answering, supported by a curve-preserving sampling method that improves results.
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Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering
Introduces Wasserstein equilibrium decoding that improves accuracy and convergence speed for small VLMs on medical VQA benchmarks by using semantic consensus instead of lexical order.