Papers › CLIP Meets Video Captioning: Concept-Aware Representation Learning Does Matter
CLIP Meets Video Captioning: Concept-Aware Representation Learning Does Matter
Bang Yang, Tong Zhang, Yuexian Zou
For video captioning, "pre-training and fine-tuning" has become a de facto paradigm, where ImageNet Pre-training (INP) is usually used to encode the video content, then a task-oriented network is fine-tuned from scratch to cope with caption generation. This paper first investigates the impact of the recently proposed CLIP (Contrastive Language-Image Pre-training) on video captioning. Through the empirical study on INP vs. CLIP, we identify the potential deficiencies of INP and explore the key factors for accurate description generation. The results show that the INP-based model is tricky to capture concepts' semantics and sensitive to irrelevant background information. By contrast, the CLIP-based model significantly improves the caption quality and highlights the importance of concept-aware representation learning. With these findings, we propose Dual Concept Detection (DCD) further to inject concept knowledge into the model during training. DCD is an auxiliary task that requires a caption model to learn the correspondence between video content and concepts and the co-occurrence relations between concepts. Experiments on MSR-VTT and VATEX demonstrate the effectiveness of DCD, and the visualization results further reveal the necessity of learning concept-aware representations.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Captioning | MSR-VTT | CLIP-DCD | BLEU-4 | 48.2 | #17 of 24 | Archive leaderboard | report |
| Video Captioning | MSR-VTT | CLIP-DCD | CIDEr | 58.7 | #17 of 24 | Archive leaderboard | report |
| Video Captioning | MSR-VTT | CLIP-DCD | METEOR | 31.3 | #17 of 24 | Archive leaderboard | report |
| Video Captioning | MSR-VTT | CLIP-DCD | ROUGE-L | 64.8 | #17 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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