Interactive Class-Agnostic Object Counting
Yifeng Huang Viresh Ranjan Minh Hoai
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/.