# RODEO: Replay for Online Object Detection

arXiv: https://arxiv.org/abs/2008.06439 · HTML: https://mlsanity.com/paper/2008.06439/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 30 of 35)

- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Replay in Deep Learning: Current Approaches and Missing Biological Elements](https://mlsanity.com/paper/2104.04132/) (2021)
- [Self-Supervised Training Enhances Online Continual Learning](https://mlsanity.com/paper/2103.14010/) (2021)
- [An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions](https://mlsanity.com/paper/2204.08817/) (2022)
- [SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection](https://mlsanity.com/paper/2402.17323/) (2024)
- [Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection](https://mlsanity.com/paper/2307.12427/) (2023)
- [Continual Object Detection: A review of definitions, strategies, and challenges](https://mlsanity.com/paper/2205.15445/) (2022)
- [SIESTA: Efficient Online Continual Learning with Sleep](https://mlsanity.com/paper/2303.10725/) (2023)
- [Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection](https://mlsanity.com/paper/2203.13903/) (2022)
- [Modeling Missing Annotations for Incremental Learning in Object Detection](https://mlsanity.com/paper/2204.08766/) (2022)
- [VLM-PL: Advanced Pseudo Labeling Approach for Class Incremental Object Detection via Vision-Language Model](https://mlsanity.com/paper/2403.05346/) (2024)
- [Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection](https://mlsanity.com/paper/2407.11499/) (2024)
- [GRASP: A Rehearsal Policy for Efficient Online Continual Learning](https://mlsanity.com/paper/2308.13646/) (2023)
- [Overcoming the Stability Gap in Continual Learning](https://mlsanity.com/paper/2306.01904/) (2023)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [Open World Object Detection: A Survey](https://mlsanity.com/paper/2410.11301/) (2024)
- [Selective Replay Enhances Learning in Online Continual Analogical Reasoning](https://mlsanity.com/paper/2103.03987/) (2021)
- [Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection](https://mlsanity.com/paper/2209.09839/) (2022)
- [Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector](https://mlsanity.com/paper/2502.05540/) (2025)
- [Re-examining Distillation For Continual Object Detection](https://mlsanity.com/paper/2204.01407/) (2022)
- [Class-Wise Buffer Management for Incremental Object Detection: An Effective Buffer Training Strategy](https://mlsanity.com/paper/2312.09139/) (2023)
- [Latent Distillation for Continual Object Detection at the Edge](https://mlsanity.com/paper/2409.01872/) (2024)
- [Cold Start Streaming Learning for Deep Networks](https://mlsanity.com/paper/2211.04624/) (2022)
- [3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding](https://mlsanity.com/paper/2212.06833/) (2022)
- [Exploiting latent representation of sparse semantic layers for improved short-term motion prediction with Capsule Networks](https://mlsanity.com/paper/2103.01644/) (2021)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track](https://mlsanity.com/paper/2110.13064/) (2021)
- [Reviewing continual learning from the perspective of human-level intelligence](https://mlsanity.com/paper/2111.11964/) (2021)
- [MultIOD: Rehearsal-free Multihead Incremental Object Detector](https://mlsanity.com/paper/2309.05334/) (2023)

## Abstract (arXiv)

Humans can incrementally learn to do new visual detection tasks, which is a huge challenge for today's computer vision systems. Incrementally trained deep learning models lack backwards transfer to previously seen classes and suffer from a phenomenon known as "catastrophic forgetting." In this paper, we pioneer online streaming learning for object detection, where an agent must learn examples one at a time with severe memory and computational constraints. In object detection, a system must output all bounding boxes for an image with the correct label. Unlike earlier work, the system described in this paper can learn this task in an online manner with new classes being introduced over time. We achieve this capability by using a novel memory replay mechanism that efficiently replays entire scenes. We achieve state-of-the-art results on both the PASCAL VOC 2007 and MS COCO datasets.