Class-agnostic counting (CAC) aims to count objects of interest from a query image given few exemplars. This task is typically addressed by extracting the features of query image and exemplars respectively with (un)shared feature extractors and by matching their feature similarity, leading to an extract-\textit{then}-match paradigm. In this work, we show that CAC can be simplified in an extract-\textit{and}-match manner, particularly using a pretrained and plain vision transformer (ViT) where feature extraction and similarity matching are executed simultaneously within the self-attention. We reveal the rationale of such simplification from a decoupled view of the self-attention and point out that the simplification is only made possible if the query and exemplar tokens are concatenated as input. The resulting model, termed CACViT, simplifies the CAC pipeline and unifies the feature spaces between the query image and exemplars. In addition, we find CACViT naturally encodes background information within self-attention, which helps reduce background disturbance. Further, to compensate the loss of the scale and the order-of-magnitude information due to resizing and normalization in ViT, we present two effective strategies for scale and magnitude embedding. Extensive experiments on the FSC147 and the CARPK datasets show that CACViT significantly outperforms state-of-the-art CAC approaches in both effectiveness (23.60% error reduction) and generalization, which suggests CACViT provides a concise and strong baseline for CAC. Code will be available.

本文提出一种使用预训练的Vision Transformer（ViT）的解决方案，实现了Class-Agnostic Counting（CAC），并将其称为CACViT。实验结果表明，CACViT在FSC147和CARPK数据集上均具有很强的鲁棒性和高效性，相较于其他现有的类别无关计数方法减少了23.60%的误差。

现成的视觉Transformer模型：针对少样本类别无关计数的出乎意料的基线