Learning To Count Everything

This week on Journal Club session Minghua Zheng will talk about a paper "Learning To Count Everything".

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://github.com/cvlab-stonybrook/LearningToCountEverything .


  • V. Ranjan, U. Sharma, T. Nguyen, M. Hoai, "Learning To Count Everything", 2021, 2021 IEEE/CV Conference on Computer Vision and Pattern Recognition (CVPR), 3393--3402

Date: 2022/02/11
Time: 14:00
Location: online

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