Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting

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

15 Scopus Citations
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Author(s)

  • Kunlin Yang
  • Xinzhu Ma
  • Junyu Gao
  • Lingbo Liu
  • Shinan Liu
  • Jun Hou
  • Shuai Yi

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationBMVC 2022 - 33rd British Machine Vision Conference Proceedings
PublisherBritish Machine Vision Association, BMVA
Number of pages14
Publication statusPublished - Nov 2022

Publication series

NameBMVC - British Machine Vision Conference Proceedings

Conference

Title33rd British Machine Vision Conference (BMVC 2022)
PlaceUnited Kingdom
CityLondon
Period21 - 24 November 2022

Abstract

Class-agnostic counting has recently emerged as a more practical counting task, which aims to predict the number and distribution of any exemplar objects, instead of counting specific categories like pedestrians or cars. However, recent methods are developed by designing suitable similarity matching rules between exemplars and query images, but ignoring the robustness of extracted features. To address this issue, we propose a scale-prior deformable convolution by integrating exemplars' information, e.g., scale, into the counting network backbone. As a result, the proposed counting network can extract semantic features of objects similar to the given exemplars and effectively filter irrelevant backgrounds. Besides, we find that traditional L2 and generalized loss are not suitable for class-agnostic counting due to the variety of object scales in different samples. Here we propose a scale-sensitive generalized loss to tackle this problem. It can adjust the cost function formulation according to the given exemplars, making the difference between prediction and ground truth more prominent. Extensive experiments show that our model obtains remarkable improvement and achieves state-of-the-art performance on a public class-agnostic counting benchmark. the source code is available at https://github.com/Elin24/SPDCN-CAC. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

Citation Format(s)

Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting. / Lin, Wei; Yang, Kunlin; Ma, Xinzhu et al.
BMVC 2022 - 33rd British Machine Vision Conference Proceedings. British Machine Vision Association, BMVA, 2022. (BMVC - British Machine Vision Conference Proceedings).

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