Abstract
We propose weakly supervised language localization networks (WSLLN) to detect events in long, untrimmed videos given language queries. To learn the correspondence between visual segments and texts, most previous methods require temporal coordinates (start and end times) of events for training, which leads to high costs of annotation. WSLLN relieves the annotation burden by training with only video-sentence pairs without accessing to temporal locations of events. With a simple end-to-end structure, WSLLN measures segment-text consistency and conducts segment selection (conditioned on the text) simultaneously. Results from both are merged and optimized as a video-sentence matching problem. Experiments on ActivityNet Captions and DiDeMo demonstrate that WSLLN achieves state-of-the-art performance.- Anthology ID:
- D19-1157
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1481–1487
- Language:
- URL:
- https://aclanthology.org/D19-1157
- DOI:
- 10.18653/v1/D19-1157
- Cite (ACL):
- Mingfei Gao, Larry Davis, Richard Socher, and Caiming Xiong. 2019. WSLLN:Weakly Supervised Natural Language Localization Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1481–1487, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- WSLLN:Weakly Supervised Natural Language Localization Networks (Gao et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/D19-1157.pdf