Know-Show: Benchmarking Video-Language Models on Spatio-Temporal Grounded Reasoning
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| Format: | Preprint |
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2025
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| _version_ | 1866911554984738816 |
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| author | Sugandhika, Chinthani Li, Chen Rajan, Deepu Fernando, Basura |
| author_facet | Sugandhika, Chinthani Li, Chen Rajan, Deepu Fernando, Basura |
| contents | Large Video-Language Models (Video-LMs) have achieved impressive progress in multimodal understanding, yet their reasoning remains weakly grounded in space and time. We present Know-Show, a new benchmark designed to evaluate spatio-temporal grounded reasoning, the ability of a model to reason about actions and their semantics while simultaneously grounding its inferences in visual and temporal evidence. Know-Show unifies reasoning and localization within a single evaluation framework consisting of five complementary scenarios across spatial (person, object, person-object, and hand-object) and temporal dimensions. Built from Charades, Action Genome, and Ego4D with 2.5K high-quality human-authored questions, the benchmark exposes significant gaps between current Video-LMs and human reasoning. To bridge this gap, we propose GRAM, a training-free plug-in that augments Video-LMs with fine-grained grounded reasoning through attention-based video token selection and explicit timestamp encoding. Extensive experiments across open and closed Video-LMs (e.g., Qwen, VideoR1, Gemini, and GPT-4o) reveal that existing models struggle to "show what they know" and vice versa. Know-Show establishes a unified standard for assessing grounded reasoning in video-language understanding and provides insights toward developing interpretable and reliable multimodal reasoning systems. We have released the dataset at https://github.com/LUNAProject22/Know-Show, and the code will be released in the same repository. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_05513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Know-Show: Benchmarking Video-Language Models on Spatio-Temporal Grounded Reasoning Sugandhika, Chinthani Li, Chen Rajan, Deepu Fernando, Basura Computer Vision and Pattern Recognition Large Video-Language Models (Video-LMs) have achieved impressive progress in multimodal understanding, yet their reasoning remains weakly grounded in space and time. We present Know-Show, a new benchmark designed to evaluate spatio-temporal grounded reasoning, the ability of a model to reason about actions and their semantics while simultaneously grounding its inferences in visual and temporal evidence. Know-Show unifies reasoning and localization within a single evaluation framework consisting of five complementary scenarios across spatial (person, object, person-object, and hand-object) and temporal dimensions. Built from Charades, Action Genome, and Ego4D with 2.5K high-quality human-authored questions, the benchmark exposes significant gaps between current Video-LMs and human reasoning. To bridge this gap, we propose GRAM, a training-free plug-in that augments Video-LMs with fine-grained grounded reasoning through attention-based video token selection and explicit timestamp encoding. Extensive experiments across open and closed Video-LMs (e.g., Qwen, VideoR1, Gemini, and GPT-4o) reveal that existing models struggle to "show what they know" and vice versa. Know-Show establishes a unified standard for assessing grounded reasoning in video-language understanding and provides insights toward developing interpretable and reliable multimodal reasoning systems. We have released the dataset at https://github.com/LUNAProject22/Know-Show, and the code will be released in the same repository. |
| title | Know-Show: Benchmarking Video-Language Models on Spatio-Temporal Grounded Reasoning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.05513 |