nVFNet-RDC: Replay and Non-Local Distillation Collaboration for Continual Object Detection
On the map: Knowledge distillation → Detection and dense prediction distillation
Abstract (arXiv)
Continual Learning (CL) focuses on developing algorithms with the ability to adapt to new environments and learn new skills. This very challenging task has generated a lot of interest in recent years, with new solutions appearing rapidly. In this paper, we propose a nVFNet-RDC approach for continual object detection. Our nVFNet-RDC consists of teacher-student models, and adopts replay and feature distillation strategies. As the 1st place solutions, we achieve 55.94% and 54.65% average mAP on the 3rd CLVision Challenge Track 2 and Track 3, respectively.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation. Builds on 2 of its 5 references that are in the map. First posted on arXiv 8 Sep 2022.
Builds on
2 of 5 references.
References · 2
- 2017Non-local Neural Networkscited by 2,293
- 2020VarifocalNet: An IoU-aware Dense Object Detectorcited by 231
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection
- 2021Contrast R-CNN for Continual Learning in Object Detection
- 2021Recent Advances of Continual Learning in Computer Vision: An Overview
- 2023Continual Detection Transformer for Incremental Object Detection
- 2024Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection
- 2023Rethinking Momentum Knowledge Distillation in Online Continual Learning
- 2022Modeling Missing Annotations for Incremental Learning in Object Detection
- 2021Response-based Distillation for Incremental Object Detection