  In the field of computer vision, the persistent presence of color bias,
resulting from fluctuations in real-world lighting and camera conditions,
presents a substantial challenge to the robustness of models. This issue is
particularly pronounced in complex wide-area surveillance scenarios, such as
person re-identification and industrial dust segmentation, where models often
experience a decline in performance due to overfitting on color information
during training, given the presence of environmental variations. Consequently,
there is a need to effectively adapt models to cope with the complexities of
camera conditions. To address this challenge, this study introduces a learning
strategy named Random Color Erasing, which draws inspiration from ensemble
learning. This strategy selectively erases partial or complete color
information in the training data without disrupting the original image
structure, thereby achieving a balanced weighting of color features and other
features within the neural network. This approach mitigates the risk of
overfitting and enhances the model's ability to handle color variation, thereby
improving its overall robustness. The approach we propose serves as an ensemble
learning strategy, characterized by robust interpretability. A comprehensive
analysis of this methodology is presented in this paper. Across various tasks
such as person re-identification and semantic segmentation, our approach
consistently improves strong baseline methods. Notably, in comparison to
existing methods that prioritize color robustness, our strategy significantly
enhances performance in cross-domain scenarios. The code available at
\url{https://github.com/layumi/Person\_reID\_baseline\_pytorch/blob/master/random\_erasing.py}
or \url{https://github.com/finger-monkey/Data-Augmentation}.
