REVITALIZE REGION FEATURE FOR DEMOCRATIZING VIDEO-LANGUAGE PRE-TRAINING OF RETRIEVAL Anonymous

Abstract

Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval. Despite the impressive results, VLP research becomes extremely expensive with the need for massive data and a long training time, preventing further explorations. In this work, we revitalize region features of sparsely sampled video clips to significantly reduce both spatial and temporal visual redundancy towards democratizing VLP research at the same time achieving state-of-the-art results. Specifically, to fully explore the potential of region features, we introduce a novel bidirectional regionword alignment regularization that properly optimizes the fine-grained relations between regions and certain words in sentences, eliminating the domain/modality disconnections between pre-extracted region features and text. Extensive results of downstream video-language retrieval tasks on four datasets demonstrate the superiority of our method on both effectiveness and efficiency, e.g., our method achieves competing results with 80% fewer data and 85% less pre-training time compared to the most efficient VLP method so far (Lei et al., 2021).

1. INTRODUCTION

Video-language pre-training (VLP) (Lei et al., 2021; Li et al., 2020a; Miech et al., 2020) that jointly learns video and language representations in a self-supervised manner has become the most popular practice to cope video-language retrieval (Lee et al., 2018; Liu et al., 2019a) . Recently, end-to-end methods (Bain et al., 2021; Zellers et al., 2021) that learn video representations from raw pixels have dominated due to their strong performance on downstream tasks. Despite significant progress, these methods are quite data-hungry due to a large number of model parameters and uncurated raw inputs. The pre-training stage turns out to be inefficient and expensive with massive pre-training data and long pre-training time, making it difficult for researchers to pursue research in VLP. Previous work (Lei et al., 2021) attempts to lower the barrier for VLP via removing visual redundancy. They point out that video clips with sparsely sampled frames are sufficient enough to capture key semantics for pre-training, since adjacent frames often contain similar scenes. The effort enables more efficient VLP with competitive downstream performances. Besides the temporal visual redundancy, we argue that, in contrast to the text with highly abstract semantics, each frame of the video clips also has heavy spatial redundancy. Towards this end, we further propose to remove the redundant spatial information in sparsely sampled video clips via the claim that a frame is actually worth around 30 objects (based experiments in Section 4.4). Specifically, we revitalize offline region features that were all the rage in imagelanguage tasks (Liu et al., 2019a) to encourage efficient VLP. Region features are generally preextracted by a pre-learned object detector (Anderson et al., 2018) . Rather than the dense and continuous visual signal of the raw pixels, the region features are sparsely distributed with the compact information of salient visual contents, which are the most useful for video-text understanding. The sparse sampling significantly reduce the complexity of attention mechanism, which enables our model to have larger capacity with less FLOPs. We further advocate "less is more" for one more step towards democratizing VLP research. As is known, methods using off-the-shelf features (Lee et al., 2018) have been phased out in visuallanguage tasks due to the inferior downstream performances. Previous work (Lei et al., 2021) at-

