Class-agnostic counting (CAC) aims to count all instances in a query image
given few exemplars. A standard pipeline is to extract visual features from
exemplars and match them with query images to infer object counts. Two
essential components in this pipeline are feature representation and similarity
metric. Existing methods either adopt a pretrained network to represent
features or learn a new one, while applying a naive similarity metric with
fixed inner product. We find this paradigm leads to noisy similarity matching
and hence harms counting performance. In this work, we propose a
similarity-aware CAC framework that jointly learns representation and
similarity metric. We first instantiate our framework with a naive baseline
called Bilinear Matching Network (BMNet), whose key component is a learnable
bilinear similarity metric. To further embody the core of our framework, we
extend BMNet to BMNet+ that models similarity from three aspects: 1)
representing the instances via their self-similarity to enhance feature
robustness against intra-class variations; 2) comparing the similarity
dynamically to focus on the key patterns of each exemplar; 3) learning from a
supervision signal to impose explicit constraints on matching results.
Extensive experiments on a recent CAC dataset FSC147 show that our models
significantly outperform state-of-the-art CAC approaches. In addition, we also
validate the cross-dataset generality of BMNet and BMNet+ on a car counting
dataset CARPK. Code is at tiny.one/BMNet

本研究提出了一个相似性感知的类不可知计数 (CAC) 框架，它同时学习了特征表示和相似度度量，并通过一个叫做 BMNet 的基线和一个叫做 BMNet + 的扩展模型对其进行了实例化，以 FSC147 数据集为实验基础，证明了该模型显著优于现有 CAC 方法。