# Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training

arXiv: https://arxiv.org/abs/2208.07075 · HTML: https://mlsanity.com/paper/2208.07075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 15 Aug 2022.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [On The Power of Curriculum Learning in Training Deep Networks](https://mlsanity.com/paper/1904.03626/) (2019)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [CNN-based Density Estimation and Crowd Counting: A Survey](https://mlsanity.com/paper/2003.12783/) (2020)
- [A Survey on Curriculum Learning](https://mlsanity.com/paper/2010.13166/) (2020)
- [Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions](https://mlsanity.com/paper/2005.07097/) (2020)
- [Dense Scale Network for Crowd Counting](https://mlsanity.com/paper/1906.09707/) (2019)
- [Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning](https://mlsanity.com/paper/2104.10461/) (2021)
- [Analyzing and Mitigating JPEG Compression Defects in Deep Learning](https://mlsanity.com/paper/2011.08932/) (2020)
- [Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications](https://mlsanity.com/paper/2207.10155/) (2022)

## Built on by (most cited first, 1 of 1)

- [PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks](https://mlsanity.com/paper/2301.12914/) (2023)

## Abstract (arXiv)

JPEG image compression algorithm is a widely used technique for image size reduction in edge and cloud computing settings. However, applying such lossy compression on images processed by deep neural networks can lead to significant accuracy degradation. Inspired by the curriculum learning paradigm, we propose a training approach called curriculum pre-training (CPT) for crowd counting on compressed images, which alleviates the drop in accuracy resulting from lossy compression. We verify the effectiveness of our approach by extensive experiments on three crowd counting datasets, two crowd counting DNN models and various levels of compression. The proposed training method is not overly sensitive to hyper-parameters, and reduces the error, particularly for heavily compressed images, by up to 19.70%.