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Video-Text Pre-training with Learned Regions

Rui Yan, Mike Zheng Shou, Yixiao Ge, Alex Jinpeng Wang, Xudong Lin, Guanyu Cai, Jinhui Tang

TL;DR

The paper addresses the limitation of frame-level video-language pre-training by introducing RegionLearner, a module that implicitly learns object-region structure from raw pixels without supervision. It quantizes visual features into semantic clusters, aggregates them into region representations with learnable masks, and models their spatio-temporal interactions to improve cross-modal alignment. Pre-trained on WebVid-2M and CC3M, RegionLearner achieves state-of-the-art results across four video-text retrieval benchmarks, with notable gains over detector-based and prior end-to-end methods. The work emphasizes efficiency and interpretability, providing visualization of learned regions and ablation analyses to validate component contributions, while also outlining limitations and future directions for robust, large-scale pre-training.

Abstract

Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information. State-of-the-art approaches extract visual features from raw pixels in an end-to-end fashion. However, these methods operate at frame-level directly and thus overlook the spatio-temporal structure of objects in video, which yet has a strong synergy with nouns in textual descriptions. In this work, we propose a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs. Given a video, our module (1) first quantizes visual features into semantic clusters, then (2) generates learnable masks and uses them to aggregate the features belonging to the same semantic region, and finally (3) models the interactions between different aggregated regions. In contrast to using off-the-shelf object detectors, our proposed module does not require explicit supervision and is much more computationally efficient. We pre-train the proposed approach on the public WebVid2M and CC3M datasets. Extensive evaluations on four downstream video-text retrieval benchmarks clearly demonstrate the effectiveness of our RegionLearner. The code will be available at https://github.com/ruiyan1995/Region_Learner.

Video-Text Pre-training with Learned Regions

TL;DR

The paper addresses the limitation of frame-level video-language pre-training by introducing RegionLearner, a module that implicitly learns object-region structure from raw pixels without supervision. It quantizes visual features into semantic clusters, aggregates them into region representations with learnable masks, and models their spatio-temporal interactions to improve cross-modal alignment. Pre-trained on WebVid-2M and CC3M, RegionLearner achieves state-of-the-art results across four video-text retrieval benchmarks, with notable gains over detector-based and prior end-to-end methods. The work emphasizes efficiency and interpretability, providing visualization of learned regions and ablation analyses to validate component contributions, while also outlining limitations and future directions for robust, large-scale pre-training.

Abstract

Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information. State-of-the-art approaches extract visual features from raw pixels in an end-to-end fashion. However, these methods operate at frame-level directly and thus overlook the spatio-temporal structure of objects in video, which yet has a strong synergy with nouns in textual descriptions. In this work, we propose a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs. Given a video, our module (1) first quantizes visual features into semantic clusters, then (2) generates learnable masks and uses them to aggregate the features belonging to the same semantic region, and finally (3) models the interactions between different aggregated regions. In contrast to using off-the-shelf object detectors, our proposed module does not require explicit supervision and is much more computationally efficient. We pre-train the proposed approach on the public WebVid2M and CC3M datasets. Extensive evaluations on four downstream video-text retrieval benchmarks clearly demonstrate the effectiveness of our RegionLearner. The code will be available at https://github.com/ruiyan1995/Region_Learner.
Paper Structure (20 sections, 6 equations, 4 figures, 6 tables)

This paper contains 20 sections, 6 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Vision-Language Pre-training.
  4. Video Representation Learning.
  5. Approach
  6. Overall
  7. Video and Text Encoder
  8. RegionLearner
  9. Quantization
  10. Aggregation
  11. Interaction
  12. Objective function
  13. Experiments
  14. Pre-training Datasets
  15. Downstream Benchmarks
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Our main motivation and results.
  • Figure 2: Overview of the proposed approach. Given a video-text pair, we encode them via the video and text encoder respectively. Region Learner is proposed to identify and leverage the implicit semantic structure of objects/regions in video, and it is performed in three steps. i) Quantization (Sec. \ref{['sec::RL_con']}): clustering the raw features into semantic clusters and replace raw features with cluster features; ii) Aggregation (Sec. \ref{['sec::RL_agg']}): we design multiple learnable masks to aggregate information with each semantic region; iii) Interaction (Sec. \ref{['sec::RL_int']}): perform spatio-temporal interactions between different aggregated regions. Finally, the video representation generated from Region Learner is used to compute the similarity with textual representation.
  • Figure 3: Effect of different number of regions and depth of interaction.
  • Figure 4: Visualization of the regions learned via the proposed Region Learner. Each group has a raw frame, the corresponding textual description, a learned map of cluster indices, and two selected learned region masks. We annotate the visual entities in colorful boxes for better understanding. The resolution of these learned maps is $14 \times 14$. (Best viewed in color.)