The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Local-Global Video-Text Interactions for Temporal Grounding
Open accessAbstract
This paper addresses the problem of text-to-video temporal grounding, which aims to identify the time interval in a video semantically relevant to a text query. We tackle this problem using a novel regression-based model that learns to extract a collection of mid-level features for semantic phrases in a text query, which corresponds to important semantic entities described in the query (e.g., actors, objects, and actions), and reflect bi-modal interactions between the linguistic features of the query and the visual features of the video in multiple levels. The proposed method effectively predicts the target time interval by exploiting contextual information from local to global during bi-modal interactions. Through in-depth ablation studies, we find out that incorporating both local and global context in video and text interactions is crucial to the accurate grounding. Our experiment shows that the proposed method outperforms the state of the arts on Charades-STA and ActivityNet Captions datasets by large margins, 7.44% and 4.61% points at Recall@tIoU=0.5 metric, respectively.
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Cites
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Learning Spatiotemporal Features with 3D Convolutional Networks
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“Thus, we adopt a regularization term introduced in [19] to enforce query attention weights to be distinct along different steps:”
Method
Cited by
Neighbor-view Enhanced Model for Vision and Language Navigation
An, D., Qi, Y., Huang, Y., Wu, Q., Wang, L., & Tan, T. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475282 · Cited by 74
Mindstorms in Natural Language-Based Societies of Mind
Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.17066 · Cited by 24
Mindstorms in Natural Language-Based Societies of Mind
Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., et al. (2025) · Computational Visual Media · doi:10.26599/cvm.2025.9450460 · Cited by 24
Metadata
- Authors
- Mun, J., Cho, M., & Han, B.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 10807-10816
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035339529
- Cited by
- 303
- References
- 39
- FWCI
- 12.57
- Type
- conference paper
- Language
- en
- Open access
- green
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