Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

18 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publicationMM ’21
Subtitle of host publicationProceedings of the 29th ACM International Conference on Multimedia
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages658–666
Publication statusPublished - Oct 2021

Publication series

NameMM - Proceedings of the ACM International Conference on Multimedia

Conference

Title29th ACM International Conference on Multimedia (MM 2021)
LocationHybrid (Onsite and Virtual)
PlaceChina
CityChengdu
Period20 - 24 October 2021

Abstract

Zero-shot cross-domain crowd counting is a challenging task where a crowd counting model is trained on a source domain (i.e., training dataset) and no additional labeled or unlabeled data is available for fine-tuning the model when testing on an unseen target domain (i.e., a different testing dataset). The generalization performance of existing crowd counting methods is typically limited due to the large gap between source and target domains. Here, we propose a novel Crowd Counting framework built upon an external Momentum Template, termed C2MoT, which enables the encoding of domain specific information via an external template representation. Specifically, the Momentum Template (MoT) is learned in a momentum updating way during offline training, and then is dynamically updated for each test image in online cross-dataset evaluation. Thanks to the dynamically updated MoT, our C2MoT effectively generates dense target correspondences that explicitly accounts for head regions, and then effectively predicts the density map based on the normalized correspondence map. Experiments on large scale datasets show that our proposed C2MoT achieves leading zero-shot cross-domain crowd counting performance without model fine-tuning, while also outperforming domain adaptation methods that use fine-tuning on target domain data. Moreover, C2MoT also obtains state-of-the-art counting performance on the source domain.

Research Area(s)

  • Zero-Shot Cross-Domain Crowd Counting, Momentum Template, Domain Adaptation

Citation Format(s)

Dynamic Momentum Adaptation for Zero-Shot Cross-Domain Crowd Counting. / Wu, Qiangqiang; Wan, Jia; Chan, Antoni B.
MM ’21: Proceedings of the 29th ACM International Conference on Multimedia. New York, NY: Association for Computing Machinery, 2021. p. 658–666 (MM - Proceedings of the ACM International Conference on Multimedia).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review