Action-and-object Aware Alignment for Partially Relevant Video Retrieval
Chuanshen Chen, Kai Zhou, Zhiquan Wen, Zeng You, Yirui Li, Tianhang Xiang, Mingkui Tan
Abstract
Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments for a given text query. This task is extremely challenging, as untrimmed videos often include numerous actions and objects unrelated to the query. However, existing methods usually struggle with fine-grained action-object modeling, limiting their retrieval performance. To tackle this challenge, we introduce Action-and-object Aware Alignment for Partially Relevant Video Retrieval (A3PRVR), a dual-branch framework designed to enhance retrieval by improving the modeling of action-object relationships. Specifically, we propose a Query-specific Deformable Temporal Attention (Q-DTA) module to effectively capture action-relevant object information in video features, while filtering out irrelevant content. Additionally, we propose an action-and-object aware alignment module to enable fine-grained textual understanding and video-text alignment. It uses action- and object-aware contrastive losses to enhance the model
BibTeX
@inproceedings{aaai2026_actionandobjecta,
title = {Action-and-object Aware Alignment for Partially Relevant Video Retrieval},
author = {Chuanshen Chen and Kai Zhou and Zhiquan Wen and Zeng You and Yirui Li and Tianhang Xiang and Mingkui Tan},
booktitle = {AAAI 2026},
year = {2026}
}