Class-Agnostic Object Counting Robust to Intraclass Diversity

摘要

Most previous works on object counting are limited to pre-defined categories. In this paper, we focus on classagnostic counting, i.e., counting object instances in an image by simply specifying a few exemplar boxes of interest. We start with an analysis on intraclass diversity and point out three factors: color, shape and scale diversity seriously hurts counting performance. Motivated by this analysis, we propose a new counter robust to high intraclass diversity, for which we propose two effective modules: Exemplar Feature Augmentation (EFA) and Edge Matching (EM). Aiming to handle diversity from all aspects, EFA generates a large variety of exemplars in the feature space based on the provided exemplars. Additionally, the edge matching branch focuses on the more reliable cue of shape, making our counter more robust to color variations. Experimental results on standard benchmarks show that our Robust Class-Agnostic Counter (RCAC) achieves state-of-the-art performance. The code is publicly available at https://github.com/Yankeegsj/RCAC.