Papers with video captioning model

2 papers
Semi-Supervised Learning for Video Captioning (2020.findings-emnlp)

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Challenge: Existing video captioning algorithms are heavily dependent on supervised training data.
Approach: They propose to train the video captioning model on labeled and unlabeled data jointly in a semi-supervised learning manner.
Outcome: The proposed model outperforms state-of-the-art semi-supervised learning approaches on VATEX, MSR-VTT and MSVD datasets.
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning (D19-1)

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Challenge: Existing studies on video captioning focus on the association relationships between multiple modalities.
Approach: They propose a video captioning model with high-order cross-modal attention (HOCA) they propose low-rank HOCA which adopts tensor decomposition to reduce the space requirement .
Outcome: The proposed model captures cross-modal interaction of different modalities and reduces space requirement.

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