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Mining Inter-Video Proposal Relations for Video Object Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)

Abstract

Recent studies have shown that, context aggregating information from proposals in different frames can clearly enhance the performance of video object detection. However, these approaches mainly exploit the intra-proposal relation within single video, while ignoring the intra-proposal relation among different videos, which can provide important discriminative cues for recognizing confusing objects. To address the limitation, we propose a novel Inter-Video Proposal Relation module. Based on a concise multi-level triplet selection scheme, this module can learn effective object representations via modeling relations of hard proposals among different videos. Moreover, we design a Hierarchical Video Relation Network (HVR-Net), by integrating intra-video and inter-video proposal relations in a hierarchical fashion. This design can progressively exploit both intra and inter contexts to boost video object detection. We examine our method on the large-scale video object detection benchmark, i.e., ImageNet VID, where HVR-Net achieves the SOTA results. Codes and models are available at https://github.com/youthHan/HVRNet.

Keywords

Video object detection Inter-Video Proposal Relation Multi-level triplet selection Hierachical Video Relation Network 

Notes

Acknowledgement

This work is partially supported by Science and Technology Service Network Initiative of Chinese Academy of Sciences (KFJ-STS-QYZX-092), Guangdong Special Support Program (2016TX03X276), Shenzhen Basic Research Program (CXB201104220032A), National Natural Science Foundation of China (61876176, U1713208), the Joint Lab of CAS-HK. This work is also partially supported by Australian Research Council Discovery Early Career Award (DE190100626).

Supplementary material

504479_1_En_26_MOESM1_ESM.pdf (455 kb)
Supplementary material 1 (pdf 455 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy SystemsShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.Faculty of Information TechnologyMonash UniversityMelbourneAustralia

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