Object Bias

Object bias, a phenomenon where machine learning models disproportionately focus on certain object categories or properties during training, is a significant challenge in computer vision. Current research investigates this bias across various tasks, including object segmentation, detection, and human-object interaction, often focusing on how data imbalances and model architectures contribute to the problem. Addressing object bias is crucial for improving the robustness and generalizability of models, leading to more accurate and reliable performance in real-world applications. This requires developing methods to mitigate the effects of biased training data and designing models less susceptible to focusing on dominant object features.