Adaptive and Background-Aware Match for Class-Agnostic Counting

Abstract

Class-Agnostic Counting (CAC) aims to count object instances in an image by simply specifying a few exemplar boxes of interest. The key challenge for CAC is how to tailor a desirable interaction between exemplar and query features. Previous CAC methods implement such interaction by solely leveraging standard global feature convolution. We find this interaction leads to under-match caused by intra-class diversity and over-match on background, which harms counting performance severely. In this work, we propose a novel feature interaction method called Adaptive and Background-aware Match (ABM) against high intra-class diversity and noisy background. Concretely, given exemplar and query features, we improve the original high-dimensional coupled spaces match to Adaptive Orthogonal subspaces Match (AOM), avoiding under-match caused by intra-class diversity. Moreover, Background-Specific Match (BSM) employs interaction between the learnable background prototype and query features to provide global background priors, making the match be aware of background regions. Additionally, we find the high scale variance among different query images leads to bad counting performance for extremely small scale objects. Object-Scale Unify (OSU) is proposed to take the size of the exemplars as the scale prior and resize query images so that all objects are at a uniform average scale. Extensive experiments on FSC-147 show that our method performs better. We also conduct extensive ablation studies to demonstrate the effectiveness of each component of our proposed method.