arXiv:2503.09399v4 Announce Type: replace-cross Abstract: Large-scale image classification datasets exhibit strong compositional biases: objects tend to be centered, appear at characteristic scales, and co-occur with class-specific context. By exploiting such biases, models attain high in-distribution accuracy but remain fragile under distribution shifts. To address this issue, we introduce ForAug, a controlled composition augmentation scheme that factorizes each training image into a foreground object and a background and recombines them to explicitly manipulate object position, object scale,
The proliferation of AI models trained on biased datasets has highlighted the fragility of current vision systems under real-world distribution shifts, making bias mitigation a pressing research area.
Improving the robustness and generalization of image classification models is crucial for deploying AI reliably in sensitive applications by addressing inherent biases in large-scale datasets.
AI models will become less susceptible to superficial compositional cues and more focused on intrinsic object features, leading to more reliable and trustworthy AI vision systems.
- · AI developers
- · Computer vision applications
- · Industries relying on AI for critical tasks
- · Deep learning research
- · Models reliant on shortcut learning
- · Datasets with unaddressed compositional biases
AI models will exhibit greater accuracy and reliability when confronted with diverse real-world visual data.
Increased trust in AI systems could accelerate adoption in sectors where robustness is paramount, such as autonomous vehicles and medical imaging.
Reduced need for extensive re-training or fine-tuning for new environments, potentially lowering the cost and time investment in AI deployment.
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Read at arXiv cs.LG