MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916430694318080 |
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| author | Nguyen, Thong Bin, Yi Wu, Xiaobao Dong, Xinshuai Hu, Zhiyuan Le, Khoi Nguyen, Cong-Duy Ng, See-Kiong Tuan, Luu Anh |
| author_facet | Nguyen, Thong Bin, Yi Wu, Xiaobao Dong, Xinshuai Hu, Zhiyuan Le, Khoi Nguyen, Cong-Duy Ng, See-Kiong Tuan, Luu Anh |
| contents | Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perfectly with each other, which might lead to video-language representations that do not accurately reflect cross-modal semantics. Moreover, previous data also possess an uneven distribution of concepts, thereby hampering the downstream performance across unpopular subjects. To address these problems, we propose MAMA, a new approach to learning video-language representations by utilizing a contrastive objective with a subtractive angular margin to regularize cross-modal representations in their effort to reach perfect similarity. Furthermore, to adapt to the non-uniform concept distribution, MAMA utilizes a multi-layer perceptron (MLP)-parameterized weighting function that maps loss values to sample weights which enable dynamic adjustment of the model's focus throughout the training. With the training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, MAMA improves video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets. The code, model, and data have been made available at https://nguyentthong.github.io/MAMA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03788 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning Nguyen, Thong Bin, Yi Wu, Xiaobao Dong, Xinshuai Hu, Zhiyuan Le, Khoi Nguyen, Cong-Duy Ng, See-Kiong Tuan, Luu Anh Computer Vision and Pattern Recognition Computation and Language Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perfectly with each other, which might lead to video-language representations that do not accurately reflect cross-modal semantics. Moreover, previous data also possess an uneven distribution of concepts, thereby hampering the downstream performance across unpopular subjects. To address these problems, we propose MAMA, a new approach to learning video-language representations by utilizing a contrastive objective with a subtractive angular margin to regularize cross-modal representations in their effort to reach perfect similarity. Furthermore, to adapt to the non-uniform concept distribution, MAMA utilizes a multi-layer perceptron (MLP)-parameterized weighting function that maps loss values to sample weights which enable dynamic adjustment of the model's focus throughout the training. With the training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, MAMA improves video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets. The code, model, and data have been made available at https://nguyentthong.github.io/MAMA. |
| title | MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2407.03788 |