YOLO-IOD: Real-Time Incremental Object Detection
Paper#Computer Vision, Object Detection, Incremental Learning🔬 Research|Analyzed: Jan 3, 2026 19:22•
Published: Dec 28, 2025 15:35
•1 min read
•ArXivAnalysis
This paper addresses the gap in real-time incremental object detection by adapting the YOLO framework. It identifies and tackles key challenges like foreground-background confusion, parameter interference, and misaligned knowledge distillation, which are critical for preventing catastrophic forgetting in incremental learning scenarios. The introduction of YOLO-IOD, along with its novel components (CPR, IKS, CAKD) and a new benchmark (LoCo COCO), demonstrates a significant contribution to the field.
Key Takeaways
- •Proposes YOLO-IOD, a real-time incremental object detection framework based on YOLO-World.
- •Introduces three key components: Conflict-Aware Pseudo-Label Refinement (CPR), Importance-based Kernel Selection (IKS), and Cross-Stage Asymmetric Knowledge Distillation (CAKD).
- •Presents LoCo COCO, a more realistic benchmark for evaluating incremental object detection.
- •Demonstrates superior performance with minimal forgetting on both conventional and LoCo COCO benchmarks.
Reference / Citation
View Original"YOLO-IOD achieves superior performance with minimal forgetting."