Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2020
Abstract
The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries. To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos. Specifically, given a complex user query, we first recursively compose a latent semantic tree to structurally describe the text query. We then design a tree-augmented query encoder to derive structure-aware query representation and a temporal attentive video encoder to model the temporal characteristics of videos. Finally, both the query and videos are mapped into a joint embedding space for matching and ranking. In this approach, we have a better understanding and modeling of the complex queries, thereby achieving a better video retrieval performance. Extensive experiments on large scale video retrieval benchmark datasets demonstrate the effectiveness of our approach.
Keywords
Multimedia retrieval, Video Search, Natural Language Understanding, Latent Tree Structure
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Data Science and Engineering
Publication
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Conference, 2020 July 25-30
First Page
1339
Last Page
1348
ISBN
9781450380164
Identifier
10.1145/3397271.3401151
Publisher
ACM
City or Country
Virtual Conference
Citation
YANG, Xun; DONG, Jianfeng; CAO, Yixin; WANG, Xun; WANG, Meng; and CHUA, Tat-Seng.
Tree-augmented cross-modal encoding for complex-query video retrieval. (2020). Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Conference, 2020 July 25-30. 1339-1348.
Available at: https://ink.library.smu.edu.sg/sis_research/7460
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/3397271.3401151
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons