Papers by Mike Zheng Shou
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning (2025.acl-long)
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Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu
| Challenge: | Large language models demonstrate remarkable capabilities across various domains, including mathematics and logic reasoning. |
| Approach: | They propose a physics-based reasoning benchmark that includes physics theorems and constraints and a Physics Solution Auto Scoring Framework to evaluate physics based reasoning in large language models. |
| Outcome: | The proposed framework enables models to achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.99%). |
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding (2023.acl-long)
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Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, W.k. Chan, Chong-Wah Ngo, Mike Zheng Shou, Nan Duan
| Challenge: | Existing work on video temporal grounding for long videos is limited by existing datasets. |
| Approach: | They propose a query-guided window selection strategy and a coarse-to-fine mechanism to speed up inference for long videos. |
| Outcome: | The proposed framework accelerates inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results. |
On Pursuit of Designing Multi-modal Transformer for Video Grounding (2021.emnlp-main)
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| Challenge: | Existing methods for video grounding are not end-to-end, i.e., they rely on time-consuming post-processing steps to refine predictions. |
| Approach: | They propose an end-to-end multi-modal Transformer model that uses two encoders and a cross-modal decoder for grounding prediction. |
| Outcome: | The proposed model is 4.9% faster than existing models and is based on a set of encodings and decoders. |
AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant (2022.findings-emnlp)
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| Challenge: | Currently, personal AI assistants on the phone and AR glasses can assist our daily life in addressing our questions like "how to adjust the date for this watch?" |
| Approach: | They propose a task that asks a question about affordance of items in our daily life . they construct a dataset that contains 3.2k multimodal questions on 1.6k video segments . |
| Outcome: | The proposed task outperforms baseline methods while still having room for improvement in the future. |
InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback (2025.findings-emnlp)
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| Challenge: | Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users. |
| Approach: | They propose an interactive framework that can be applied to any LMM and assess their interactive intelligence with human users. |
| Outcome: | The proposed framework can be applied to any LMM and dataset to assess interactive intelligence with human users. |