MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
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arXiv:2507.23511v3 Announce Type: replace-cross Abstract: While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because current benchmarks, limited by data annotations and evaluation metrics, fail to reliably distinguish between generic and highly detailed model outputs. To this end, this work introduces MECAT, a Multi-Expert Constructed Benchmark for Fine-Grained Audio Understanding T
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Title: MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
Abstract:
arXiv:2507.23511v3 Announce Type: replace-cross Abstract: While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because current benchmarks, limited by data annotations and evaluation metrics, fail to reliably distinguish between generic and highly detailed model outputs. To this end, this work introduces MECAT, a Multi-Expert Constructed Benchmark for Fine-Grained Audio Understanding T
Abstract:
arXiv:2507.23511v3 Announce Type: replace-cross Abstract: While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because current benchmarks, limited by data annotations and evaluation metrics, fail to reliably distinguish between generic and highly detailed model outputs. To this end, this work introduces MECAT, a Multi-Expert Constructed Benchmark for Fine-Grained Audio Understanding T
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