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thenewstack.io·23h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Proposal Refinement for Few-Shot Object Detection
TL;DR
Few-shot object detection faces challenges due to an unbalanced distribution of region proposals between novel and base classes. To address this, a proposal refinement approach is introduced during different training phases.
✦ Why It Matters
Engineers can implement proposal refinement techniques to improve few-shot object detection in their applications.
Key Takeaways
- Utilize proposal refinement to improve detection accuracy.
- Incorporate refinement loss in base training phases.
- Add auxiliary branches to RPN for better novel class proposals.
- Aim for a 1% to 6% performance boost on benchmarks.
How It Works
The proposal refinement approach addresses class imbalance by introducing a refinement loss during the base training phase, which increases the model's sensitivity to novel classes. In the fine-tuning phase, an auxiliary refinement branch is integrated into the Region Proposal Network (RPN) to enhance the generation of proposals for these novel classes, effectively rebalancing the proposal distribution.