Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval

TL;DR

This paper introduces RIVRL, a novel video representation learning method inspired by human reading strategies, which improves text-to-video retrieval by capturing both overview and detailed video features, achieving state-of-the-art results.

Contribution

Proposes a reading-strategy inspired dual-branch framework for video representation learning that enhances cross-modal retrieval performance.

Findings

Achieves new state-of-the-art on TGIF and VATEX datasets.

Performs comparably or better than models trained on large-scale datasets.

Effectively captures both overview and detailed video information.

Abstract

This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset.