# SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation

arXiv: https://arxiv.org/abs/2012.15439 · HTML: https://mlsanity.com/paper/2012.15439/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 30 of 33)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation](https://mlsanity.com/paper/2204.02136/) (2022)
- [Continual Detection Transformer for Incremental Object Detection](https://mlsanity.com/paper/2304.03110/) (2023)
- [Recent Advances of Continual Learning in Computer Vision: An Overview](https://mlsanity.com/paper/2109.11369/) (2021)
- [SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection](https://mlsanity.com/paper/2402.17323/) (2024)
- [Continual Object Detection: A review of definitions, strategies, and challenges](https://mlsanity.com/paper/2205.15445/) (2022)
- [VLM-PL: Advanced Pseudo Labeling Approach for Class Incremental Object Detection via Vision-Language Model](https://mlsanity.com/paper/2403.05346/) (2024)
- [BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation](https://mlsanity.com/paper/2407.09083/) (2024)
- [Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection](https://mlsanity.com/paper/2403.14797/) (2024)
- [DCA: Dividing and Conquering Amnesia in Incremental Object Detection](https://mlsanity.com/paper/2503.15295/) (2025)
- [Dynamic Object Queries for Transformer-based Incremental Object Detection](https://mlsanity.com/paper/2407.21687/) (2024)
- [Replay Consolidation with Label Propagation for Continual Object Detection](https://mlsanity.com/paper/2409.05650/) (2024)
- [DIODE: Dilatable Incremental Object Detection](https://mlsanity.com/paper/2108.05627/) (2021)
- [Latent Distillation for Continual Object Detection at the Edge](https://mlsanity.com/paper/2409.01872/) (2024)
- [Addressing the Challenges of Open-World Object Detection](https://mlsanity.com/paper/2303.14930/) (2023)
- [IOR: Inversed Objects Replay for Incremental Object Detection](https://mlsanity.com/paper/2406.04829/) (2024)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [MultIOD: Rehearsal-free Multihead Incremental Object Detector](https://mlsanity.com/paper/2309.05334/) (2023)
- [Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection](https://mlsanity.com/paper/2503.04688/) (2025)
- [DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic](https://mlsanity.com/paper/2506.21260/) (2025)
- [EW-DETR: Evolving World Object Detection via Incremental Low-Rank DEtection TRansformer](https://mlsanity.com/paper/2602.20985/) (2026)
- [Focus, Align, and Sustain: Counteracting Gradient Dilution in Incremental Object Detection](https://mlsanity.com/paper/2606.15253/) (2026)
- [Symbiosis-Inspired Knowledge Distillation for Incremental Object Detection](https://mlsanity.com/paper/2607.13452/) (2026)
- [Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection](https://mlsanity.com/paper/2607.17340/) (2026)
- [Response-based Distillation for Incremental Object Detection](https://mlsanity.com/paper/2110.13471/) (2021)
- [Refined Response Distillation for Class-Incremental Player Detection](https://mlsanity.com/paper/2305.00620/) (2023)
- [DualTeacher: Bridging Coexistence of Unlabelled Classes for Semi-supervised Incremental Object Detection](https://mlsanity.com/paper/2401.05362/) (2023)
- [Hierarchical Neural Collapse Detection Transformer for Class Incremental Object Detection](https://mlsanity.com/paper/2506.08562/) (2025)
- [Index-Aligned Query Distillation for Transformer-based Incremental Object Detection](https://mlsanity.com/paper/2508.11339/) (2025)
- [Parameterized Prompt for Incremental Object Detection](https://mlsanity.com/paper/2510.27316/) (2025)

## Abstract (arXiv)

Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task -- a problem known as catastrophic forgetting. In this paper, we address this issue in the context of anchor-free object detection, which is a new trend in computer vision as it is simple, fast, and flexible. Simply adapting current incremental learning strategies fails on these anchor-free detectors due to lack of consideration of their specific model structures. To deal with the challenges of incremental learning on anchor-free object detectors, we propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID). In addition, a novel evaluation metric is proposed to better assess the performance of detectors under incremental learning conditions. By selective distilling at the proper locations and further transferring additional instance relation knowledge, our method demonstrates significant advantages on the benchmark datasets PASCAL VOC and COCO.