Object-Aware Multi-Branch Relation Networks for Spatio-Temporal Video Grounding

Author:

Zhang Zhu1,Zhao Zhou1,Lin Zhijie1,Huai Baoxing2,Yuan Jing2

Affiliation:

1. College of Computer Science, Zhejiang University, China

2. Huawei Cloud & AI, China

Abstract

Spatio-temporal video grounding aims to retrieve the spatio-temporal tube of a queried object according to the given sentence. Currently, most existing grounding methods are restricted to well-aligned segment-sentence pairs. In this paper, we explore spatio-temporal video grounding on unaligned data and multi-form sentences. This challenging task requires to capture critical object relations to identify the queried target. However, existing approaches cannot distinguish notable objects and remain in ineffective relation modeling between unnecessary objects. Thus, we propose a novel object-aware multi-branch relation network for object-aware relation discovery. Concretely, we first devise multiple branches to develop object-aware region modeling, where each branch focuses on a crucial object mentioned in the sentence. We then propose multi-branch relation reasoning to capture critical object relationships between the main branch and auxiliary branches. Moreover, we apply a diversity loss to make each branch only pay attention to its corresponding object and boost multi-branch learning. The extensive experiments show the effectiveness of our proposed method.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Learning Feature Semantic Matching for Spatio-Temporal Video Grounding;IEEE Transactions on Multimedia;2024

2. Temporal Sentence Grounding in Videos: A Survey and Future Directions;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-08

3. WINNER: Weakly-supervised hIerarchical decompositioN and aligNment for spatio-tEmporal video gRounding;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

4. Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding;IEEE Transactions on Multimedia;2023

5. Weakly-Supervised Video Object Grounding via Causal Intervention;IEEE Transactions on Pattern Analysis and Machine Intelligence;2022

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