Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning

TL;DR

This paper introduces a two-stage text-to-video retrieval framework that uses multi-granularity visual features and a coarse-to-fine approach, significantly improving efficiency while maintaining high accuracy.

Contribution

The paper proposes a novel coarse-to-fine retrieval architecture with a parameter-free text-gated interaction block and Pearson Constraint, enhancing feature utilization and retrieval speed.

Findings

Achieves comparable performance to state-of-the-art methods

Nearly 50 times faster retrieval speed

Effective multi-granularity visual feature learning

Abstract

In recent years, text-to-video retrieval methods based on CLIP have experienced rapid development. The primary direction of evolution is to exploit the much wider gamut of visual and textual cues to achieve alignment. Concretely, those methods with impressive performance often design a heavy fusion block for sentence (words)-video (frames) interaction, regardless of the prohibitive computation complexity. Nevertheless, these approaches are not optimal in terms of feature utilization and retrieval efficiency. To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase. To better leverage the multi-granularity features, we devise a two-stage retrieval architecture in the retrieval phase. This solution ingeniously balances the coarse and fine granularity of retrieval content. Moreover, it also strikes a harmonious equilibrium between retrieval effectiveness and efficiency. Specifically, in training phase, we design a parameter-free text-gated interaction block (TIB) for fine-grained video representation learning and embed an extra Pearson Constraint to optimize cross-modal representation learning. In retrieval phase, we use coarse-grained video representations for fast recall of top-k candidates, which are then reranked by fine-grained video representations. Extensive experiments on four benchmarks demonstrate the efficiency and effectiveness. Notably, our method achieves comparable performance with the current state-of-the-art methods while being nearly 50 times faster.