Papers by Kangqi Li
Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) have potential for cross-modal understanding . but extending MLLM to handle diverse modalities introduces two challenges . |
| Approach: | They propose a dual-stage compression mechanism to reduce the number of modality tokens per modality and condense it into a single, compact token sequence. |
| Outcome: | Experiments show that Flex-M3 outperforms its counterpart trained on only full-modality data. |
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? (2025.emnlp-main)
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Yiyang Zhou, Linjie Li, Shi Qiu, Zhengyuan Yang, Yuyang Zhao, Siwei Han, Yangfan He, Kangqi Li, Haonian Ji, Zihao Zhao, Haibo Tong, Lijuan Wang, Huaxiu Yao
| Challenge: | Existing video benchmarks often resemble image-based questions with scans of only a few key frames, without deep temporal reasoning. |
| Approach: | They propose a video benchmark to assess whether large vision-language models can genuinely think with videos rather than perform superficial frame-level analysis. |
| Outcome: | The proposed benchmark consists of 3,269 videos and over 4,342 highly visual-centric questions across 11 categories, including Trajectory Analysis, Temporal Reasoning, and Forensics Detection. |