Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


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 present a novel 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. Our proposed training method is not overly sensitive to hyper-parameters, and reduces the error, particularly for heavily compressed images, by up to 19.70%.
Original languageEnglish
Title of host publication2023 IEEE Symposium Series on Computational Intelligence (SSCI)
Number of pages6
Publication date2023
ISBN (Electronic)978-1-6654-3065-4, 978-1-6654-3064-7
Publication statusPublished - 2023
SeriesProceedings (IEEE Symposium Series on Computational Intelligence)


Dive into the research topics of 'Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training'. Together they form a unique fingerprint.

Cite this