Papers by Yuankai Qi
Diagnosing Vision-and-Language Navigation: What Really Matters (2022.naacl-main)
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Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Wang, Qi Wu, Miguel Eckstein, William Yang Wang
| Challenge: | Existing models claim to be able to align object tokens with specific visual targets, but there are non-negligible gaps between the two. |
| Approach: | They conduct diagnostic experiments to examine how the agents perceive multimodal input by ablation diagnostics input data. |
| Outcome: | The results show that indoor and outdoor navigation agents refer to object and direction tokens when making decisions. |
The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning (2026.findings-eacl)
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| Challenge: | Existing methods for zero-shot video captioning focus on one key aspect of the scene and ignore the rest of the visual input. |
| Approach: | They propose a novel textual prompting strategy for zero-shot video captioning that uses a category-aware retrieval mechanism to promote prompt diversity while ensuring visual relevance. |
| Outcome: | The proposed method outperforms existing methods on in-domain and cross-domain settings. |
StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing (2024.findings-acl)
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Gaoxiang Cong, Yuankai Qi, Liang Li, Amin Beheshti, Zhedong Zhang, Anton Hengel, Ming-Hsuan Yang, Chenggang Yan, Qingming Huang
| Challenge: | Existing methods for movie dubbing break phonemes in scripts, resulting in incomplete phoneme pronunciation and poor identity stability. |
| Approach: | They propose a method that switches dubbing learning from frame level to phoneme level . it uses a multimodal style adaptor to learn pronunciation style from audio . |
| Outcome: | The proposed method improves on two benchmarks, V2C and Grid, and is available on github. |