Learning To Count Everything

• URL: http://arxiv.org/abs/2104.08391v1
• Date: Fri, 16 Apr 2021 22:45:58 GMT
• Title: Learning To Count Everything
• Authors: Viresh Ranjan, Udbhav Sharma, Thu Nguyen, Minh Hoai
• Abstract summary: We are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. 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.
• Score: 16.1159048148031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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