Papers by Chenggang Yan
Semantic Relation-aware Difference Representation Learning for Change Captioning (2021.findings-acl)
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| Challenge: | Existing methods to describe semantic change in images with distractors are difficult to learn . |
| Approach: | They propose a semantic relation-aware difference representation learning network to explicitly learn the difference representation in the existence of distractors. |
| Outcome: | The proposed network achieves state-of-the-art performance on CLEVR-Change and Spot-the -Diff datasets. |
Context-aware Difference Distilling for Multi-change Captioning (2024.acl-long)
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| Challenge: | Existing methods for multi-change captioning are difficult because it requires a higher level of cognition to reason an arbitrary number of changes. |
| Approach: | They propose a context-aware difference distilling network to capture all genuine changes for yielding sentences. |
| Outcome: | The proposed network captures all genuine changes for yielding sentences on three public datasets. |
Rˆ3Net:Relation-embedded Representation Reconstruction Network for Change Captioning (2021.emnlp-main)
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| Challenge: | Existing work on change captioning uses a natural language sentence to describe disagreement between two images. |
| Approach: | They propose a Relation-embedded Representation Reconstruction Network to distinguish real change from clutter and irrelevant changes. |
| Outcome: | The proposed method achieves state-of-the-art on two public datasets. |
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. |