#few-shot-detection News & Analysis

Proposal Refinement for Few-Shot Object Detection

Researchers propose a proposal refinement approach for few-shot object detection that addresses the unbalanced distribution of region proposals between novel and base classes. The method introduces a refinement loss during base training and a refinement branch for RPN during fine-tuning, achieving 1-6% performance improvements on benchmarks without additional inference costs.