MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning
<|reference_start|>MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning: 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.<|reference_end|>
@article{nguyen2024mama:,
    title={MAMA: Meta-optimized Angular Margin Contrastive Framework for
  Video-Language Representation Learning},
    author={Thong Nguyen, Yi Bin, Xiaobao Wu, Xinshuai Dong, Zhiyuan Hu, Khoi Le,
  Cong-Duy Nguyen, See-Kiong Ng, and Luu Anh Tuan},
    journal={arXiv preprint arXiv:2407.03788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03788},
    primaryClass={cs.CV cs.CL}
}