Proposal Refinement for Few-Shot Object Detection

2026-06-08

AI Summary

This work addresses the imbalance in region proposal distributions between base and novel classes in few-shot object detection by proposing a staged proposal refinement strategy. During base-class training, a refinement loss is introduced to enhance the model’s sensitivity to novel classes. In the fine-tuning stage, a lightweight refinement branch is added to the Region Proposal Network (RPN) to generate higher-quality proposals for novel classes, effectively balancing the proposal distribution. Notably, this approach is the first to explicitly tackle distribution shift at the region proposal level. It achieves significant performance gains without increasing inference overhead, surpassing existing methods by 1%–6% across multiple mainstream benchmarks and establishing a new state of the art.

Abstract

Few-shot object detection has gained widely attention in recent years. Some excellent algorithms have been proposed to handle this task. However, most of these algorithms rely on the performance of few-shot classification. Unlike previous attempts, our work focuses on the problem of unbalanced distribution of region proposals between the novel classes and the base classes. In order to alleviate this unbalanced distribution, we propose the proposal refinement approach for different training phases. Specifically, refinement loss is designed for the base training phase to enhance sensitivity of the model to novel classes, and refinement branch is introduced as an auxiliary branch for RPN (Region Proposal Networks) to generate more novel proposals in the fine-tuning phase. By rebalancing the proposal distribution, the proposed approach outperforms the baselines methods by roughly 1\%$\sim$6\% on current benchmarks without increasing any inference time. Through extensive experiments, we prove that we establish a new state-of-the-art method for the few-shot object detection task.

Problem

• few-shot object detection
• region proposals
• class imbalance
• proposal distribution
• novel classes

Innovation

• proposal refinement
• few-shot object detection
• region proposal network
• class imbalance
• refinement loss