DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918495177932800 |
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| author | Park, Yohan Ha, Hyunwoo Jo, Wonjun Oh, Tae-Hyun |
| author_facet | Park, Yohan Ha, Hyunwoo Jo, Wonjun Oh, Tae-Hyun |
| contents | Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light conditions at night or in dark environments, a core necessity that has been largely overlooked. To address this underexplored challenge, we present DarkQA, an open-source benchmark for evaluating perceptual primitives under multi-level low-light conditions in embodied scenarios. DarkQA evaluates single-view egocentric observations across controlled degradation levels, isolating low-light perceptual failures before they are entangled with complex embodied tasks. The benchmark contains 9.4K deterministically generated and verifiable question-image pairs spanning five visual-primitive families. A key design feature of DarkQA is its physical fidelity: visual degradations are modeled in linear RAW space, simulating physics-based illumination drop and sensor noise followed by an ISP-inspired rendering pipeline; we further validate the synthesis against real paired low-light camera data. We evaluate representative VLMs and Low-Light Image Enhancement (LLIE) preprocessing methods. Results show consistent VLM degradation under low illumination and sensor noise, while LLIE provides severity-dependent but unstable recovery. We demonstrate the utility of DarkQA by evaluating a wide range of state-of-the-art VLMs and Low-Light Image Enhancement (LLIE) models, and systematically reveal VLMs' limitations when operating under these challenging visual conditions. Our code and benchmark dataset will be released upon acceptance. Project website: https://darkqa-benchmark.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes Park, Yohan Ha, Hyunwoo Jo, Wonjun Oh, Tae-Hyun Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light conditions at night or in dark environments, a core necessity that has been largely overlooked. To address this underexplored challenge, we present DarkQA, an open-source benchmark for evaluating perceptual primitives under multi-level low-light conditions in embodied scenarios. DarkQA evaluates single-view egocentric observations across controlled degradation levels, isolating low-light perceptual failures before they are entangled with complex embodied tasks. The benchmark contains 9.4K deterministically generated and verifiable question-image pairs spanning five visual-primitive families. A key design feature of DarkQA is its physical fidelity: visual degradations are modeled in linear RAW space, simulating physics-based illumination drop and sensor noise followed by an ISP-inspired rendering pipeline; we further validate the synthesis against real paired low-light camera data. We evaluate representative VLMs and Low-Light Image Enhancement (LLIE) preprocessing methods. Results show consistent VLM degradation under low illumination and sensor noise, while LLIE provides severity-dependent but unstable recovery. We demonstrate the utility of DarkQA by evaluating a wide range of state-of-the-art VLMs and Low-Light Image Enhancement (LLIE) models, and systematically reveal VLMs' limitations when operating under these challenging visual conditions. Our code and benchmark dataset will be released upon acceptance. Project website: https://darkqa-benchmark.github.io |
| title | DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Robotics |
| url | https://arxiv.org/abs/2512.24985 |