Abstract
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 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, we propose 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, we improve video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets.
Abstract (translated)
数据质量是决定视频语言表示学习有效性的首要因素。然而，之前的数据中通常不完美地匹配视频文本对，这可能导致不准确地反映跨模态语义的视频语言表示。此外，以前的数据也具有不均匀的概念分布，从而阻碍了对于非流行主题的下游性能。为了应对这些问题，我们提出了一个具有细差角边缘的对比性目标来对跨模态表示进行正则化，以达到完美的相似度。此外，为了适应概念分布的不均匀性，我们提出了一个多层感知器（MLP）参数化加权函数，该函数将损失值映射到样本权重，从而在训练过程中动态调整模型的关注点。在训练过程中，我们通过少量的无偏元数据和由大型视觉语言模型生成的视频文本数据来指导训练，从而改善视频语言表示并实现对于常见视频问题回答和文本-视频检索数据集的优越性能。
URL
https://arxiv.org/abs/2407.03788
