# PROB: Probabilistic Objectness for Open World Object Detection

arXiv: https://arxiv.org/abs/2212.01424 · HTML: https://mlsanity.com/paper/2212.01424/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 40 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 25 of 25)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [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)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (2019)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Incremental Learning of Object Detectors without Catastrophic Forgetting](https://mlsanity.com/paper/1708.06977/) (2017)
- [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)
- [Learning Open-World Object Proposals without Learning to Classify](https://mlsanity.com/paper/2108.06753/) (2021)
- [Class-agnostic Object Detection with Multi-modal Transformer](https://mlsanity.com/paper/2111.11430/) (2021)
- [Open-World Entity Segmentation](https://mlsanity.com/paper/2107.14228/) (2021)
- [Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN](https://mlsanity.com/paper/2003.03901/) (2020)
- [Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint](https://mlsanity.com/paper/2204.07965/) (2022)
- [Memory Efficient Class-Incremental Learning for Image Classification](https://mlsanity.com/paper/2008.01411/) (2020)
- [Opening up Open-World Tracking](https://mlsanity.com/paper/2104.11221/) (2021)
- [Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity](https://mlsanity.com/paper/2204.06107/) (2022)
- [UC-OWOD: Unknown-Classified Open World Object Detection](https://mlsanity.com/paper/2207.11455/) (2022)
- [Learning to Detect Every Thing in an Open World](https://mlsanity.com/paper/2112.01698/) (2021)
- [Revisiting Open World Object Detection](https://mlsanity.com/paper/2201.00471/) (2022)

## Built on by (most cited first, 30 of 57)

- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Transformer-Based Visual Segmentation: A Survey](https://mlsanity.com/paper/2304.09854/) (2023)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [Random Boxes Are Open-world Object Detectors](https://mlsanity.com/paper/2307.08249/) (2023)
- [From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models](https://mlsanity.com/paper/2506.09930/) (2025)
- [OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision](https://mlsanity.com/paper/2405.17913/) (2024)
- [Bridge Past and Future: Overcoming Information Asymmetry in Incremental Object Detection](https://mlsanity.com/paper/2407.11499/) (2024)
- [Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection](https://mlsanity.com/paper/2306.14291/) (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)
- [Open World Object Detection in the Era of Foundation Models](https://mlsanity.com/paper/2312.05745/) (2023)
- [YOLO-UniOW: Efficient Universal Open-World Object Detection](https://mlsanity.com/paper/2412.20645/) (2024)
- [SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving](https://mlsanity.com/paper/2305.06940/) (2023)
- [3D Indoor Instance Segmentation in an Open-World](https://mlsanity.com/paper/2309.14338/) (2023)
- [Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection](https://mlsanity.com/paper/2403.14797/) (2024)
- [Unsupervised Recognition of Unknown Objects for Open-World Object Detection](https://mlsanity.com/paper/2308.16527/) (2023)
- [Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector](https://mlsanity.com/paper/2502.05540/) (2025)
- [MarineDet: Towards Open-Marine Object Detection](https://mlsanity.com/paper/2310.01931/) (2023)
- [Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes](https://mlsanity.com/paper/2404.07664/) (2024)
- [From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects](https://mlsanity.com/paper/2411.18207/) (2024)
- [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)
- [Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection](https://mlsanity.com/paper/2510.00303/) (2025)
- [Detecting Every Object from Events](https://mlsanity.com/paper/2404.05285/) (2024)
- [From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning](https://mlsanity.com/paper/2502.05843/) (2025)
- [Addressing the Challenges of Open-World Object Detection](https://mlsanity.com/paper/2303.14930/) (2023)
- [Towards Energy-Efficiency by Navigating the Trilemma of Energy, Latency, and Accuracy](https://mlsanity.com/paper/2409.04018/) (2024)
- [UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework](https://mlsanity.com/paper/2412.09229/) (2024)

## Abstract (arXiv)

Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - which can be classified and incrementally learned. In standard OD, object proposals not overlapping with a labeled object are automatically classified as background. Therefore, simply applying OD methods to OWOD fails as unknown objects would be predicted as background. The challenge of detecting unknown objects stems from the lack of supervision in distinguishing unknown objects and background object proposals. Previous OWOD methods have attempted to overcome this issue by generating supervision using pseudo-labeling - however, unknown object detection has remained low. Probabilistic/generative models may provide a solution for this challenge. Herein, we introduce a novel probabilistic framework for objectness estimation, where we alternate between probability distribution estimation and objectness likelihood maximization of known objects in the embedded feature space - ultimately allowing us to estimate the objectness probability of different proposals. The resulting Probabilistic Objectness transformer-based open-world detector, PROB, integrates our framework into traditional object detection models, adapting them for the open-world setting. Comprehensive experiments on OWOD benchmarks show that PROB outperforms all existing OWOD methods in both unknown object detection (~ 2× unknown recall) and known object detection (~ 10% mAP). Our code will be made available upon publication at https://github.com/orrzohar/PROB.