Cross-Modal and Hierarchical Modeling of Video and Text

@inproceedings{Zhang2018CrossModalAH,
  title={Cross-Modal and Hierarchical Modeling of Video and Text},
  author={Bowen Zhang and Hexiang Hu and Fei Sha},
  booktitle={ECCV},
  year={2018}
}
Visual data and text data are composed of information at multiple granularities. A video can describe a complex scene that is composed of multiple clips or shots, where each depicts a semantically coherent event or action. Similarly, a paragraph may contain sentences with different topics, which collectively conveys a coherent message or story. In this paper, we investigate the modeling techniques for such hierarchical sequential data where there are correspondences across multiple modalities… 
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87 Citations
Highly Influential Citations
9
Background Citations
31
Methods Citations
31
Results Citations
4

87 Citations

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Citation Type

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TLDR
The HierArchical Multi-Modal EncodeR (HAMMER) is proposed that encodes a video at both the coarse-grained clip level and the fine- grained frame level to extract information at different scales based on multiple subtasks, namely, video retrieval, segment temporal localization, and masked language modeling.
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TLDR
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TLDR
This work encode the dataset data in a way that takes into account the query’s relevant information, and achieves state-of-the-art results for both sentence-clip and video-text by a sizable margin across five different datasets: ActivityNet, DiDeMo, YouCook2, MSR-VTT, and LSMDC.
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TLDR
This paper proposes a dual deep encoding network that encodes videos and queries into powerful dense representations of their own and introduces hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space.
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TLDR
This review categorizes and describes the state-of-the-art techniques for the video-to-text problem and analyzes how the most reported benchmark datasets have been created, showing their drawbacks and strengths for the problem requirements.
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