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
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or \url{this https URL}.

本研究提出了一种名为随机颜色擦除的学习策略，通过选择性地擦除训练数据中的部分或完整的颜色信息，实现了对神经网络内的颜色特征和其他特征进行平衡加权，从而提高模型处理颜色变化的能力，增强其整体鲁棒性。该策略在诸如人员再识别和语义分割等任务中不断改进了强基准方法，在跨领域场景中显著提升性能。