Robustness on Minghui Chen

VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

Tue, 01 Feb 2022 00:00:00 +0000

Authors: Minghui Chen, Cheng Wen, Feng Zheng, Fengxiang He, Ling Shao

Published in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2022)

Abstract

Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptions. However, the samples produced by popular augmentation strategies deviate significantly from the underlying data manifold. As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. The proposed VITA consists of two complementary parts, tangent transfer and integration of multi-source vicinal samples. The tangent transfer creates initial augmented samples for improving corruption robustness. The integration employs a generative model to characterize the underlying manifold built by vicinal samples, facilitating the generation of on-manifold samples. Our proposed VITA significantly outperforms the current state-of-the-art augmentation methods, demonstrated in extensive experiments on corruption benchmarks.

Corruption Invariant Person Re-Identification

Thu, 30 Dec 2021 00:00:00 +0000

Abstract

Benchmarks for Corruption Invariant Person Re-Identification

Wed, 01 Dec 2021 00:00:00 +0000

Authors: Minghui Chen (Equal contribution), Zhiqiang Wang (Equal contribution), Feng Zheng

Published in: Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2021)

Abstract

When deploying person re-identification (ReID) model in safety-critical applications, it is pivotal to understanding the robustness of the model against a diverse array of image corruptions. However, current evaluations of person ReID only consider the performance on clean datasets and ignore images in various corrupted scenarios. In this work, we comprehensively establish five ReID benchmarks for learning corruption invariant representation. In the field of ReID, we are the first to conduct an exhaustive study on corruption invariant learning in single- and cross-modality datasets, including Market-1501, CUHK03, MSMT17, RegDB, SYSU-MM01. After reproducing and examining the robustness performance of 21 recent ReID methods, we have some observations, 1) transformer-based models are more robust towards corrupted images, compared with CNN-based models, 2) increasing the probability of random erasing (a commonly used augmentation method) hurts model corruption robustness, 3) cross-dataset generalization improves with corruption robustness increases. By analyzing the above observations, we propose a strong baseline on both single- and cross-modality ReID datasets which achieves improved robustness against diverse corruptions. Our codes are available on github( https://github.com/MinghuiChen43/CIL-ReID).