Class agnostic counting (CAC) is a vision task that can be used to count the
total occurrence number of any given reference objects in the query image. The
task is usually formulated as a density map estimation problem through
similarity computation among a few image samples of the reference object and
the query image. In this paper, we point out a severe issue of the existing CAC
framework: Given a multi-class setting, models don't consider reference images
and instead blindly match all dominant objects in the query image. Moreover,
the current evaluation metrics and dataset cannot be used to faithfully assess
the model's generalization performance and robustness. To this end, we discover
that the combination of mosaic augmentation with generalized loss is essential
for addressing the aforementioned issue of CAC models to count objects of
majority (i.e. dominant objects) regardless of the references. Furthermore, we
introduce a new evaluation protocol and metrics for resolving the problem
behind the existing CAC evaluation scheme and better benchmarking CAC models in
a more fair manner. Besides, extensive evaluation results demonstrate that our
proposed recipe can consistently improve the performance of different CAC
models. The code will be released upon acceptance.

在现有的无类别计数 (CAC) 框架中，我们指出了一个严重的问题：在多类别环境下，模型不考虑参考图像，而是盲目匹配查询图像中的所有主要对象。此外，现有的评估指标和数据集不能真实地评估模型的泛化性能和鲁棒性。为了解决这个问题，我们发现将马赛克增强与广义损失相结合对于解决无类别计数模型中计算多数对象（即主要对象）的问题至关重要。此外，我们引入了一种新的评估协议和指标，以解决现有无类别计数评估方案中存在的问题，并以更公平的方式对无类别计数模型进行基准测试。此外，广泛的评估结果表明，我们提出的方法可以持续改善不同无类别计数模型的性能。我们将在论文被接受后发布代码。