Iterative Correlation-based Feature Refinement for Few-shot Counting

• URL: http://arxiv.org/abs/2201.08959v1
• Date: Sat, 22 Jan 2022 03:27:11 GMT
• Title: Iterative Correlation-based Feature Refinement for Few-shot Counting
• Authors: Zhiyuan You, Kai Yang, Wenhan Luo, Xin Lu, Lei Cui, Xinyi Le
• Abstract summary: Few-shot counting aims to count objects of any class in an image given only a few exemplars of the same class. Existing correlation-based few-shot counting approaches suffer from the coarseness and low semantic level of the correlation. We propose an iterative framework to progressively refine the exemplar-related features based on the correlation between the image and exemplars.
• Score: 35.27237393354539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot counting aims to count objects of any class in an image given only a few exemplars of the same class. Existing correlation-based few-shot counting approaches suffer from the coarseness and low semantic level of the correlation. To solve these problems, we propose an iterative framework to progressively refine the exemplar-related features based on the correlation between the image and exemplars. Then the density map is predicted from the final refined feature map. The iterative framework includes a Correlation Distillation module and a Feature Refinement module. During the iterations, the exemplar-related features are gradually refined, while the exemplar-unrelated features are suppressed, benefiting few-shot counting where the exemplar-related features are more important. Our approach surpasses all baselines significantly on few-shot counting benchmark FSC-147. Surprisingly, though designed for general class-agnostic counting, our approach still achieves state-of-the-art performance on car counting benchmarks CARPK and PUCPR+, and crowd counting benchmarks UCSD and Mall. We also achieve competitive performance on crowd counting benchmark ShanghaiTech. The code will be released soon.

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