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.
The llama 3 herd of models,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
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
-
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.
-
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.