Papers › Object Relational Graph with Teacher-Recommended Learning for Video Captioning
Object Relational Graph with Teacher-Recommended Learning for Video Captioning
Ziqi Zhang, Yaya Shi, Chunfeng Yuan, Bing Li, Peijin Wang, Weiming Hu, Zheng-Jun Zha
Taking full advantage of the information from both vision and language is critical for the video captioning task. Existing models lack adequate visual representation due to the neglect of interaction between object, and sufficient training for content-related words due to long-tailed problems. In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model. The ELM generates more semantically similar word proposals which extend the ground-truth words used for training to deal with the long-tailed problem. Experimental evaluations on three benchmarks: MSVD, MSR-VTT and VATEX show the proposed ORG-TRL system achieves state-of-the-art performance. Extensive ablation studies and visualizations illustrate the effectiveness of our system.
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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 | VATEX | ORG-TRL | BLEU-4 | 32.1 | #9 of 10 | Archive leaderboard | report |
| Video Captioning | VATEX | ORG-TRL | CIDEr | 49.7 | #9 of 10 | Archive leaderboard | report |
| Video Captioning | VATEX | ORG-TRL | METEOR | 22.2 | #9 of 10 | Archive leaderboard | report |
| Video Captioning | VATEX | ORG-TRL | ROUGE-L | 48.9 | #9 of 10 | 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.
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