Research Paper#Computer Vision, Object Detection, Semi-Supervised Learning, Infrared Imaging🔬 ResearchAnalyzed: Jan 3, 2026 16:27
Scalpel-SAM: Semi-Supervised Infrared Object Detection
Analysis
This paper addresses the critical problem of data scarcity in infrared small object detection (IR-SOT) by proposing a semi-supervised approach leveraging SAM (Segment Anything Model). The core contribution lies in a novel two-stage paradigm using a Hierarchical MoE Adapter to distill knowledge from SAM and transfer it to lightweight downstream models. This is significant because it tackles the high annotation cost in IR-SOT and demonstrates performance comparable to or exceeding fully supervised methods with minimal annotations.
Key Takeaways
- •Addresses data scarcity in IR-SOT using a semi-supervised approach.
- •Leverages SAM as a teacher model.
- •Proposes a two-stage paradigm: Prior-Guided Knowledge Distillation and Deployment-Oriented Knowledge Transfer.
- •Employs a Hierarchical MoE Adapter.
- •Achieves performance comparable to or surpassing fully supervised methods with minimal annotations.
Reference
“Experiments demonstrate that with minimal annotations, our paradigm enables downstream models to achieve performance comparable to, or even surpassing, their fully supervised counterparts.”