This article presents two frameworks for combating issues with fine-grained vehicle recognition (FGVR) caused by image noise. The first, a progressive multi-task anti-noise learning (PMAL) framework, improves recognition accuracy by considering image denoising as an additional task in image recognition. The second, a progressive multi-task distilling (PMD) framework, transfers the knowledge of the PMAL-trained model into the original backbone network. These combined frameworks outperform previous methods in recognition accuracy on several FGVR datasets without any additional overheads over the original backbone networks.
Publication date: 26 Jan 2024
Project Page: https://github.com/Dichao-Liu/Anti-noise FGVR
Paper: https://arxiv.org/pdf/2401.14336
