# Revisiting Open World Object Detection

arXiv: https://arxiv.org/abs/2201.00471 · HTML: https://mlsanity.com/paper/2201.00471/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 60 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Categorical Depth Distribution Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2103.01100/) (2021)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Counterfactual VQA: A Cause-Effect Look at Language Bias](https://mlsanity.com/paper/2006.04315/) (2020)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Incremental Learning of Object Detectors without Catastrophic Forgetting](https://mlsanity.com/paper/1708.06977/) (2017)
- [OpenGAN: Open-Set Recognition via Open Data Generation](https://mlsanity.com/paper/2104.02939/) (2021)
- [Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation](https://mlsanity.com/paper/2104.04691/) (2021)
- [OW-DETR: Open-world Detection Transformer](https://mlsanity.com/paper/2112.01513/) (2021)
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://mlsanity.com/paper/1710.06677/) (2017)
- [Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2101.12609/) (2021)
- [Counterfactual Zero-Shot and Open-Set Visual Recognition](https://mlsanity.com/paper/2103.00887/) (2021)
- [Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection](https://mlsanity.com/paper/2103.01903/) (2021)
- [Open-world Learning and Application to Product Classification](https://mlsanity.com/paper/1809.06004/) (2018)
- [A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts](https://mlsanity.com/paper/2105.00290/) (2021)
- [Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling](https://mlsanity.com/paper/2111.01004/) (2021)
- [Densely Nested Top-Down Flows for Salient Object Detection](https://mlsanity.com/paper/2102.09133/) (2021)
- [Robust Region Feature Synthesizer for Zero-Shot Object Detection](https://mlsanity.com/paper/2201.00103/) (2022)
- [Objects in Semantic Topology](https://mlsanity.com/paper/2110.02687/) (2021)
- [Transformation Driven Visual Reasoning](https://mlsanity.com/paper/2011.13160/) (2020)

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

- [PROB: Probabilistic Objectness for Open World Object Detection](https://mlsanity.com/paper/2212.01424/) (2022)
- [Towards Open-Set Object Detection and Discovery](https://mlsanity.com/paper/2204.05604/) (2022)
- [UC-OWOD: Unknown-Classified Open World Object Detection](https://mlsanity.com/paper/2207.11455/) (2022)
- [CapDet: Unifying Dense Captioning and Open-World Detection Pretraining](https://mlsanity.com/paper/2303.02489/) (2023)
- [EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries](https://mlsanity.com/paper/2212.06969/) (2022)
- [Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects](https://mlsanity.com/paper/2303.13769/) (2023)
- [Open World Object Detection: A Survey](https://mlsanity.com/paper/2410.11301/) (2024)
- [DiPEx: Dispersing Prompt Expansion for Class-Agnostic Object Detection](https://mlsanity.com/paper/2406.14924/) (2024)
- [Towards Generalized Few-Shot Open-Set Object Detection](https://mlsanity.com/paper/2210.15996/) (2022)
- [SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving](https://mlsanity.com/paper/2305.06940/) (2023)
- [USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model](https://mlsanity.com/paper/2306.02275/) (2023)
- [NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios](https://mlsanity.com/paper/2309.09518/) (2023)
- [SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector](https://mlsanity.com/paper/2312.08653/) (2023)
- [Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis](https://mlsanity.com/paper/2407.15763/) (2024)
- [Tunable Hybrid Proposal Networks for the Open World](https://mlsanity.com/paper/2208.11050/) (2022)
- [Open-World Weakly-Supervised Object Localization](https://mlsanity.com/paper/2304.08271/) (2023)
- [OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss](https://mlsanity.com/paper/2311.14939/) (2023)
- [Rethinking Open-Set Object Detection: Issues, a New Formulation, and Taxonomy](https://mlsanity.com/paper/2207.09775/) (2022)
- [Detecting the open-world objects with the help of the Brain](https://mlsanity.com/paper/2303.11623/) (2023)
- [Vehicle Detection Performance in Nordic Region](https://mlsanity.com/paper/2403.15017/) (2024)
- [OW-CLIP: Data-Efficient Visual Supervision for Open-World Object Detection via Human-AI Collaboration](https://mlsanity.com/paper/2507.19870/) (2025)
- [Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector](https://mlsanity.com/paper/2311.02342/) (2023)
- [YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery](https://mlsanity.com/paper/2404.00257/) (2024)
- [OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery](https://mlsanity.com/paper/2404.10865/) (2024)
- [On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution Data](https://mlsanity.com/paper/2411.04586/) (2024)
- [Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions](https://mlsanity.com/paper/2501.07133/) (2025)
- [Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection](https://mlsanity.com/paper/2507.13085/) (2025)
- [Towards Open World Detection: A Survey](https://mlsanity.com/paper/2508.16527/) (2025)
- [Information Theory in Open-world Machine Learning Foundations, Frameworks, and Future Direction](https://mlsanity.com/paper/2510.15422/) (2025)
- [Towards Unconstrained Human-Object Interaction](https://mlsanity.com/paper/2604.14069/) (2026)

## Abstract (arXiv)

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones. We find that although the only previous OWOD work constructively puts forward to the OWOD definition, the experimental settings are unreasonable with the illogical benchmark, confusing metric calculation, and inappropriate method. In this paper, we rethink the OWOD experimental setting and propose five fundamental benchmark principles to guide the OWOD benchmark construction. Moreover, we design two fair evaluation protocols specific to the OWOD problem, filling the void of evaluating from the perspective of unknown classes. Furthermore, we introduce a novel and effective OWOD framework containing an auxiliary Proposal ADvisor (PAD) and a Class-specific Expelling Classifier (CEC). The non-parametric PAD could assist the RPN in identifying accurate unknown proposals without supervision, while CEC calibrates the over-confident activation boundary and filters out confusing predictions through a class-specific expelling function. Comprehensive experiments conducted on our fair benchmark demonstrate that our method outperforms other state-of-the-art object detection approaches in terms of both existing and our new metrics. Our benchmark and code are available at https://github.com/RE-OWOD/RE-OWOD.