A minority-friendly learning method for object detection: joint optimization of oversampling-driven data balancing and loss reconstruction
A minority-friendly learning method for object detection is proposed through the joint optimization of oversampling-driven data rebalancing and loss reconstruction, and a minority-oriented loss reformulation mechanism is designed to strengthen the contribution of underrepresented classes during training.