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VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models

Jiapeng Wang, Chengyu Wang, Kunzhe Huang, Jun Huang, Lianwen Jin

TL;DR

VideoCLIP-XL addresses the gap in video-language models' ability to understand long textual descriptions. It introduces a pipeline that automatically collects a large VILD dataset of video-long descriptions, a Text-similarity-guided Primary Component Matching (TCPM) to adapt representation learning, and two description-ranking tasks (Detail-aware Description Ranking and Hallucination-aware Description Ranking) to shape faithful long-text understanding. It also introduces the LVDR benchmark for evaluating long-description ranking and demonstrates state-of-the-art performance on standard text-video retrieval benchmarks and the LVDR task. The work shows that combining large-scale long-description data with adaptive feature selection and targeted ranking tasks yields substantial gains for long-text video understanding.

Abstract

Contrastive Language-Image Pre-training (CLIP) has been widely studied and applied in numerous applications. However, the emphasis on brief summary texts during pre-training prevents CLIP from understanding long descriptions. This issue is particularly acute regarding videos given that videos often contain abundant detailed contents. In this paper, we propose the VideoCLIP-XL (eXtra Length) model, which aims to unleash the long-description understanding capability of video CLIP models. Firstly, we establish an automatic data collection system and gather a large-scale VILD pre-training dataset with VIdeo and Long-Description pairs. Then, we propose Text-similarity-guided Primary Component Matching (TPCM) to better learn the distribution of feature space while expanding the long description capability. We also introduce two new tasks namely Detail-aware Description Ranking (DDR) and Hallucination-aware Description Ranking (HDR) for further understanding improvement. Finally, we construct a Long Video Description Ranking (LVDR) benchmark for evaluating the long-description capability more comprehensively. Extensive experimental results on widely-used text-video retrieval benchmarks with both short and long descriptions and our LVDR benchmark can fully demonstrate the effectiveness of our method.

VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models

TL;DR

VideoCLIP-XL addresses the gap in video-language models' ability to understand long textual descriptions. It introduces a pipeline that automatically collects a large VILD dataset of video-long descriptions, a Text-similarity-guided Primary Component Matching (TCPM) to adapt representation learning, and two description-ranking tasks (Detail-aware Description Ranking and Hallucination-aware Description Ranking) to shape faithful long-text understanding. It also introduces the LVDR benchmark for evaluating long-description ranking and demonstrates state-of-the-art performance on standard text-video retrieval benchmarks and the LVDR task. The work shows that combining large-scale long-description data with adaptive feature selection and targeted ranking tasks yields substantial gains for long-text video understanding.

Abstract

Contrastive Language-Image Pre-training (CLIP) has been widely studied and applied in numerous applications. However, the emphasis on brief summary texts during pre-training prevents CLIP from understanding long descriptions. This issue is particularly acute regarding videos given that videos often contain abundant detailed contents. In this paper, we propose the VideoCLIP-XL (eXtra Length) model, which aims to unleash the long-description understanding capability of video CLIP models. Firstly, we establish an automatic data collection system and gather a large-scale VILD pre-training dataset with VIdeo and Long-Description pairs. Then, we propose Text-similarity-guided Primary Component Matching (TPCM) to better learn the distribution of feature space while expanding the long description capability. We also introduce two new tasks namely Detail-aware Description Ranking (DDR) and Hallucination-aware Description Ranking (HDR) for further understanding improvement. Finally, we construct a Long Video Description Ranking (LVDR) benchmark for evaluating the long-description capability more comprehensively. Extensive experimental results on widely-used text-video retrieval benchmarks with both short and long descriptions and our LVDR benchmark can fully demonstrate the effectiveness of our method.
Paper Structure (18 sections, 9 equations, 7 figures, 7 tables)

This paper contains 18 sections, 9 equations, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. VIdeo & Long-Description (VILD) Dataset
  5. Text-similarity-guided Primary Component Matching (TCPM)
  6. Two Description Ranking Tasks
  7. The New LVDR Benchmark
  8. Experiments
  9. Implementation Detail
  10. Performance Comparison
  11. Ablation Study
  12. Conclusion
  13. Appendix
  14. Details of VILD Data Generation
  15. Details of Data Statistics
  16. ...and 3 more sections

Figures (7)

  • Figure 1: The automatic data collection system for our VILD dataset. Desc. is short for description.
  • Figure 2: The proposed text-similarity-guided primary component matching (TPCM) technique.
  • Figure 3: Examples of text samples generated for (a) hallucination-aware and (b) detail-aware description ranking tasks. Blue and green words refer to replaced hallucination content and detailed content to be deleted, respectively. Best viewed in color.
  • Figure 4: Qualitative examples on our LVDR benchmark. We calculate the cosine similarities between different long descriptions and the same video using video CLIP models. Descriptions are sorted based on these similarities in descending order after retaining 8 decimal places.
  • Figure 5: TPCM can dynamically adjust the dimension of the attribute vectors that need to be retained during pre-training. Dim. is short for dimension.
  • ...and 2 more figures