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 https://yifehuang97.github.io/ICACountProjectPage/.

翻译：我们提出了一种新颖的交互式类别无关目标计数框架，人类用户可通过交互式反馈提升计数器的准确性。该框架包含两大核心组件：用于收集反馈的用户友好型可视化工具，以及高效融入反馈的机制。在每次迭代中，我们生成密度图呈现当前预测结果，并将其分割为若干非重叠区域，每个区域内的目标数量可轻松验证。用户可通过选择存在明显计数错误的区域，并指定该区域内预估目标数量的范围来提供反馈。为改进计数结果，我们设计了一种新型自适应损失函数，强制视觉计数器在用户指定范围内输出预测计数。为实现高效自适应，我们提出了一种可适配于任意基于密度的视觉计数器的精炼模块，在自适应过程中仅需更新该模块的参数。在FSCD-LVIS和FSC-147这两个具有挑战性的类别无关目标计数基准测试中，我们的方法仅需极少量用户输入，即可将多个最先进视觉计数器的平均绝对误差降低约30%至40%。项目主页：https://yifehuang97.github.io/ICACountProjectPage/。