Partial annotation-based video moment retrieval via iterative learning

• Main Authors: JI, Wei, LIANG, Renjie, LIAO, Lizi, FEI, Hao, FENG, Fuli
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Current; Coarse guidance; Iterative learning; Labour-intensive; Performance; Pseudo label; Query video; Retrieval methods; Time-stamp; Video moment retrieval; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8585; https://ink.library.smu.edu.sg/context/sis_research/article/9588/viewcontent/Partial_Annotation_based_Video_Moment_Retrieval_via_Iterative_Learning.pdf
• Institution: Singapore Management University

Given a descriptive language query, Video Moment Retrieval (VMR) aims to seek the corresponding semantic-consistent moment clip in the video, which is represented as a pair of the start and end timestamps. Although current methods have achieved satisfying performance, training these models heavily relies on the fully-annotated VMR datasets. Nonetheless, precise video temporal annotations are extremely labor-intensive and ambiguous due to the diverse preferences of different annotators.Although there are several works trying to explore weakly supervised VMR tasks with scattered annotated frames as labels, there is still much room to improve in terms of accuracy. Therefore, we design a new setting of VMR where users can easily point to small segments of non-controversy video moments and our proposed method can automatically fill in the remaining parts based on the video and query semantics. To support this, we propose a new framework named Video Moment Retrieval via Iterative Learning (VMRIL). It treats the partial temporal region as the seed, then expands the pseudo label by iterative training. In order to restrict the expansion with reasonable boundaries, we utilize a pretrained video action localization model to provide coarse guidance of potential video segments. Compared with other VMR methods, our VMRIL achieves a trade-off between satisfying performance and annotation efficiency. Experimental results show that our proposed method can achieve the SOTA performance in the weakly supervised VMR setting, and are even comparable with some fully-supervised VMR methods but with much less annotation cost.