Support-set bottlenecks for video-text representation learning (2010.02824v2)

Abstract: The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples that are known to be related, such as text and video from the same sample, and pushes away the representations of all other pairs. We posit that this last behaviour is too strict, enforcing dissimilar representations even for samples that are semantically-related -- for example, visually similar videos or ones that share the same depicted action. In this paper, we propose a novel method that alleviates this by leveraging a generative model to naturally push these related samples together: each sample's caption must be reconstructed as a weighted combination of other support samples' visual representations. This simple idea ensures that representations are not overly-specialized to individual samples, are reusable across the dataset, and results in representations that explicitly encode semantics shared between samples, unlike noise contrastive learning. Our proposed method outperforms others by a large margin on MSR-VTT, VATEX and ActivityNet, and MSVD for video-to-text and text-to-video retrieval.

Support-Set Bottlenecks for Video-Text Representation Learning

The paper addresses the limitations inherent in noise contrastive learning (NCL) for video-text representation learning. NCL, while effective in associating related samples, often enforces dissimilarity between semantically related samples, which can negatively impact downstream tasks like video retrieval. This paper proposes an innovative method that leverages generative models to overcome this issue, introducing the concept of using support-set bottlenecks to guide the learning process.

The core proposition of this paper is the utilization of a generative model as a counterbalance to the strict separations imposed by NCL. By reconstructing captions through a weighted combination of visual representations from a support set, the paper fosters the encoding of shared semantics while ensuring that representations do not become overly specialized. This approach ensures that representations are adaptable and capture the nuances of semantic similarities across different samples. Such adaptation is vital in producing versatile representations that facilitate enhanced retrieval performance.

The experimental findings presented are compelling. The proposed method significantly outperforms existing models on benchmark datasets like MSR-VTT, VATEX, ActivityNet, and MSVD. The results underscore the effectiveness of combining discriminative and generative tasks to derive video-text representations that are both robust and semantically rich.

From a practical standpoint, the implications of such a method are notable. The ability to enhance retrieval tasks through better representation learning could improve applications in various fields, from media retrieval to video understanding and beyond. This approach also opens potential pathways for integrating additional modalities in multi-modal learning, extending beyond the confines of video-text pairs to more complex data interactions.

The theoretical implications of adjusting the tight constraints of NCL through generative models emphasize a shift towards more holistic representation learning. This shift highlights the importance of designing models that consider both discriminative and generative potentials, possibly leading to the development of more intuitive and context-aware AI systems.

Looking to the future, this framework sets the stage for further exploration into scalable and efficient methods of integrating generative objectives into other domains of representation learning. As AI systems continue to evolve, the balance between discriminative precision and generative flexibility will become increasingly crucial.

Overall, this paper contributes a significant methodological innovation to the domain of video-text representation learning by cleverly integrating generative tasks with traditional contrastive learning to enhance the semantic aptitude of learned representations.