DarkQA is a new benchmark that measures vision-language model performance on basic visual questions under controlled low-light degradations modeled from real camera physics.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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PrivFusion deploys agents to cluster semantically similar features and iteratively recommend transformations for harmonizing heterogeneous structured datasets in a privacy-preserving manner, evaluated on four COVID-19 datasets.
The work creates NIABench and an LLM-plus-scoring-model framework that enables robots to deliver proactive assistance during human multi-step activities while avoiding interruptions and reducing human effort.
citing papers explorer
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DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes
DarkQA is a new benchmark that measures vision-language model performance on basic visual questions under controlled low-light degradations modeled from real camera physics.
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PrivFusion: A Privacy-preserving Multi-Agent Framework for Harmonizing Distributed Datasets
PrivFusion deploys agents to cluster semantically similar features and iteratively recommend transformations for harmonizing heterogeneous structured datasets in a privacy-preserving manner, evaluated on four COVID-19 datasets.
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Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance
The work creates NIABench and an LLM-plus-scoring-model framework that enables robots to deliver proactive assistance during human multi-step activities while avoiding interruptions and reducing human effort.