Abstract
Multi-channel video-language retrieval require models to understand information from different modalities (e.g. video+question, video+speech) and real-world knowledge to correctly link a video with a textual response or query. Fortunately, multimodal contrastive models have been shown to be highly effective at aligning entities in images/videos and text, e.g., CLIP; text contrastive models have been extensively studied recently for their strong ability of producing discriminative sentence embeddings, e.g., SimCSE. Their abilities are exactly needed by multi-channel video-language retrieval. However, it is not clear how to quickly adapt these two lines of models to multi-channel video-language retrieval-style tasks. In this paper, we identify a principled model design space with two axes: how to represent videos and how to fuse video and text information. Based on categorization of recent methods, we investigate the options of representing videos using continuous feature vectors or discrete text tokens; for the fusion method, we explore a multimodal transformer or a pretrained contrastive text model. We extensively evaluate the four combinations on five video-language datasets. We surprisingly find that discrete text tokens coupled with a pretrained contrastive text model yields the best performance. This combination can even outperform state-of-the-art on the iVQA dataset without the additional training on millions of video-language data. Further analysis shows that this is because representing videos as text tokens captures the key visual information with text tokens that are naturally aligned with text models and the text models obtained rich knowledge during contrastive pretraining process. All the empirical analysis we obtain for the four variants establishes a solid foundation for future research on leveraging the rich knowledge of pretrained contrastive models.
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URL
https://arxiv.org/abs/2206.02082
