  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.
