Papers by Leigang Qu
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives (2024.findings-acl)
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Thong Nguyen, Yi Bin, Junbin Xiao, Leigang Qu, Yicong Li, Jay Zhangjie Wu, Cong-Duy Nguyen, See-Kiong Ng, Anh Tuan Luu
| Challenge: | Existing video-language understanding systems with human-like senses can mimic both our linguistic medium and visual environment with temporal dynamics. |
| Approach: | They propose to develop video-language understanding systems with human-like senses . they summarize their methods and highlight challenges associated with them . |
| Outcome: | The proposed models perform well in a variety of tasks and domains. |
Generative Cross-Modal Retrieval: Memorizing Images in Multimodal Language Models for Retrieval and Beyond (2024.acl-long)
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| Challenge: | Recent advances in generative language models have demonstrated their ability to memorize knowledge from documents and recall knowledge to respond to user queries effectively. |
| Approach: | They propose to enable multimodal large language models to memorize and recall images within their parameters. |
| Outcome: | The proposed model performs well even with large-scale image candidate sets. |