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

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.