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Reading-Strategy Inspired Visual Representation Learning for Text-to-Video Retrieval.
- Source :
- IEEE Transactions on Circuits & Systems for Video Technology; Aug2022, Vol. 32 Issue 8, p5680-5694, 15p
- Publication Year :
- 2022
-
Abstract
- This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos. The success of this task depends on cross-modal representation learning that projects both videos and sentences into common spaces for semantic similarity computation. In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval. Inspired by the reading strategy of humans, we propose a Reading-strategy Inspired Visual Representation Learning (RIVRL) to represent videos, which consists of two branches: a previewing branch and an intensive-reading branch. The previewing branch is designed to briefly capture the overview information of videos, while the intensive-reading branch is designed to obtain more in-depth information. Moreover, the intensive-reading branch is aware of the video overview captured by the previewing branch. Such holistic information is found to be useful for the intensive-reading branch to extract more fine-grained features. Extensive experiments on three datasets are conducted, where our model RIVRL achieves a new state-of-the-art on TGIF and VATEX. Moreover, on MSR-VTT, our model using two video features shows comparable performance to the state-of-the-art using seven video features and even outperforms models pre-trained on the large-scale HowTo100M dataset. Code is available at https://github.com/LiJiaBei-7/rivrl. [ABSTRACT FROM AUTHOR]
- Subjects :
- VISUAL learning
VIDEOS
FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 32
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 158333582
- Full Text :
- https://doi.org/10.1109/TCSVT.2022.3150959