GCNet: Probing Self-Similarity Learning for Generalized Counting Network

• URL: http://arxiv.org/abs/2302.05132v1
• Date: Fri, 10 Feb 2023 09:31:37 GMT
• Title: GCNet: Probing Self-Similarity Learning for Generalized Counting Network
• Authors: Mingjie Wang and Yande Li and Jun Zhou and Graham W. Taylor and Minglun Gong
• Abstract summary: Generalized Counting Network (GCNet) is developed to recognize adaptive exemplars within the whole images. GCNet is capable of adaptively capturing them through a carefully-designed self-similarity learning strategy. It performs on par with existing exemplar-dependent methods and shows stunning cross-dataset generality on crowd-specific datasets.
• Score: 24.09746233447471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The class-agnostic counting (CAC) problem has caught increasing attention recently due to its wide societal applications and arduous challenges. To count objects of different categories, existing approaches rely on user-provided exemplars, which is hard-to-obtain and limits their generality. In this paper, we aim to empower the framework to recognize adaptive exemplars within the whole images. A zero-shot Generalized Counting Network (GCNet) is developed, which uses a pseudo-Siamese structure to automatically and effectively learn pseudo exemplar clues from inherent repetition patterns. In addition, a weakly-supervised scheme is presented to reduce the burden of laborious density maps required by all contemporary CAC models, allowing GCNet to be trained using count-level supervisory signals in an end-to-end manner. Without providing any spatial location hints, GCNet is capable of adaptively capturing them through a carefully-designed self-similarity learning strategy. Extensive experiments and ablation studies on the prevailing benchmark FSC147 for zero-shot CAC demonstrate the superiority of our GCNet. It performs on par with existing exemplar-dependent methods and shows stunning cross-dataset generality on crowd-specific datasets, e.g., ShanghaiTech Part A, Part B and UCF_QNRF.

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