Background Bias

Background bias, the tendency of machine learning models to rely on irrelevant background information in images rather than the target object, significantly hinders model generalization and robustness. Current research focuses on mitigating this bias through various techniques, including data augmentation (e.g., background removal or replacement), algorithmic approaches like contrastive learning and Layer-wise Relevance Propagation (LRP) optimization to re-weight samples or focus model attention, and architectural modifications to improve feature extraction. Addressing background bias is crucial for improving the reliability and real-world applicability of computer vision models across diverse applications, from wildlife identification to medical image analysis and product retrieval.