We propose a novel framework for interactive class-agnostic object counting,
where a human user can interactively provide feedback to improve the accuracy
of a counter. Our framework consists of two main components: a user-friendly
visualizer to gather feedback and an efficient mechanism to incorporate it. In
each iteration, we produce a density map to show the current prediction result,
and we segment it into non-overlapping regions with an easily verifiable number
of objects. The user can provide feedback by selecting a region with obvious
counting errors and specifying the range for the estimated number of objects
within it. To improve the counting result, we develop a novel adaptation loss
to force the visual counter to output the predicted count within the
user-specified range. For effective and efficient adaptation, we propose a
refinement module that can be used with any density-based visual counter, and
only the parameters in the refinement module will be updated during adaptation.
Our experiments on two challenging class-agnostic object counting benchmarks,
FSCD-LVIS and FSC-147, show that our method can reduce the mean absolute error
of multiple state-of-the-art visual counters by roughly 30% to 40% with minimal
user input. Our project can be found at
this https URL

我们提出了一个新颖的互动式无类别对象计数框架，其中人类用户可以交互地提供反馈以提高计数器的准确性。我们的框架包括两个主要组成部分：一个用户友好的可视化工具来收集反馈，以及一个高效的机制来整合它。