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

• URL: http://arxiv.org/abs/2410.00741v2
• Date: Fri, 4 Oct 2024 16:10:38 GMT
• Title: VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models
• Authors: Jiapeng Wang, Chengyu Wang, Kunzhe Huang, Jun Huang, Lianwen Jin,
• Abstract summary: Contrastive Language-Image Pre-training (CLIP) has been widely studied and applied in numerous applications. The emphasis on brief summary texts during pre-training prevents CLIP from understanding long descriptions. We propose the VideoCLIP-XL (eXtra Length) model, which aims to unleash the long-description understanding capability of video CLIP models.
• Score: 38.429386337415785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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