# Fine-Grained Counting with Crowd-Sourced Supervision

arXiv: https://arxiv.org/abs/2205.11398 · HTML: https://mlsanity.com/paper/2205.11398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 7 of 7)

- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds](https://mlsanity.com/paper/1808.01050/) (2018)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization](https://mlsanity.com/paper/2001.03360/) (2020)
- [CNN-based Density Estimation and Crowd Counting: A Survey](https://mlsanity.com/paper/2003.12783/) (2020)
- [Crowdsourcing in Computer Vision](https://mlsanity.com/paper/1611.02145/) (2016)
- [Fine-Grained Crowd Counting](https://mlsanity.com/paper/2007.06146/) (2020)

## Abstract (arXiv)

Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis further. In this work we study the potential to do so on the challenging task of fine-grained counting. As opposed to the standard crowd counting task, fine-grained counting also involves classifying attributes of individuals in dense crowds. We introduce a new dataset from animal ecology to enable this study that contains 1.7M crowd-sourced annotations of 8 fine-grained classes. It is the largest available dataset for fine-grained counting and the first to enable the study of the task with crowd-sourced annotations. We introduce methods for generating aggregate "ground truths" from the collected annotations, as well as a counting method that can utilize the aggregate information. Our method improves results by 8% over a comparable baseline, indicating the potential for algorithms to learn fine-grained counting using crowd-sourced supervision.