# Expanding Low-Density Latent Regions for Open-Set Object Detection

arXiv: https://arxiv.org/abs/2203.14911 · HTML: https://mlsanity.com/paper/2203.14911/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 55 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 30 of 43)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Meta-Learning for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1803.00676/) (2018)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [Learning Confidence for Out-of-Distribution Detection in Neural Networks](https://mlsanity.com/paper/1802.04865/) (2018)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Parametric Contrastive Learning](https://mlsanity.com/paper/2107.12028/) (2021)
- [Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals](https://mlsanity.com/paper/2102.06191/) (2021)

## Built on by (most cited first, 30 of 38)

- [Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery](https://mlsanity.com/paper/2310.19776/) (2023)
- [Contrastive Conditional Latent Diffusion for Audio-visual Segmentation](https://mlsanity.com/paper/2307.16579/) (2023)
- [Open-Set Recognition in the Age of Vision-Language Models](https://mlsanity.com/paper/2403.16528/) (2024)
- [Dual Decision Improves Open-Set Panoptic Segmentation](https://mlsanity.com/paper/2207.02504/) (2022)
- [Activate and Reject: Towards Safe Domain Generalization under Category Shift](https://mlsanity.com/paper/2310.04724/) (2023)
- [Towards Generalized Few-Shot Open-Set Object Detection](https://mlsanity.com/paper/2210.15996/) (2022)
- [Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning](https://mlsanity.com/paper/2211.11530/) (2022)
- [Unsupervised Recognition of Unknown Objects for Open-World Object Detection](https://mlsanity.com/paper/2308.16527/) (2023)
- [Precise Event Spotting in Sports Videos: Solving Long-Range Dependency and Class Imbalance](https://mlsanity.com/paper/2503.00147/) (2025)
- [USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model](https://mlsanity.com/paper/2306.02275/) (2023)
- [Rethinking Polyp Segmentation from an Out-of-Distribution Perspective](https://mlsanity.com/paper/2306.07792/) (2023)
- [Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection](https://mlsanity.com/paper/2408.15580/) (2024)
- [Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization](https://mlsanity.com/paper/2403.09433/) (2024)
- [Boosting Few-Shot Open-Set Object Detection via Prompt Learning and Robust Decision Boundary](https://mlsanity.com/paper/2406.18443/) (2024)
- [Deep Learning for Sports Video Event Detection: Tasks, Datasets, Methods, and Challenges](https://mlsanity.com/paper/2505.03991/) (2025)
- [Tunable Hybrid Proposal Networks for the Open World](https://mlsanity.com/paper/2208.11050/) (2022)
- [UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework](https://mlsanity.com/paper/2412.09229/) (2024)
- [Rethinking Open-Set Object Detection: Issues, a New Formulation, and Taxonomy](https://mlsanity.com/paper/2207.09775/) (2022)
- [Generalized Open-World Semi-Supervised Object Detection](https://mlsanity.com/paper/2307.15710/) (2023)
- [Incremental Object Detection with CLIP](https://mlsanity.com/paper/2310.08815/) (2023)
- [O1O: Grouping of Known Classes to Identify Unknown Objects as Odd-One-Out](https://mlsanity.com/paper/2410.07514/) (2024)
- [Open-Set Object Detection By Aligning Known Class Representations](https://mlsanity.com/paper/2412.20701/) (2024)
- [DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection](https://mlsanity.com/paper/2606.24805/) (2026)
- [Online Open-set Semi-supervised Object Detection with Dual Competing Head](https://mlsanity.com/paper/2305.13802/) (2023)
- [Detecting Out-of-distribution Objects Using Neuron Activation Patterns](https://mlsanity.com/paper/2307.16433/) (2023)
- [Wasserstein Distance-based Expansion of Low-Density Latent Regions for Unknown Class Detection](https://mlsanity.com/paper/2401.05594/) (2024)
- [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)
- [Class-balanced Open-set Semi-supervised Object Detection for Medical Images](https://mlsanity.com/paper/2408.12355/) (2024)
- [Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection](https://mlsanity.com/paper/2411.13001/) (2024)

## Abstract (arXiv)

Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categories are often misclassified to existing known classes. In this work, we propose to identify unknown objects by separating high/low-density regions in the latent space, based on the consensus that unknown objects are usually distributed in low-density latent regions. As traditional threshold-based methods only maintain limited low-density regions, which cannot cover all unknown objects, we present a novel Open-set Detector (OpenDet) with expanded low-density regions. To this aim, we equip OpenDet with two learners, Contrastive Feature Learner (CFL) and Unknown Probability Learner (UPL). CFL performs instance-level contrastive learning to encourage compact features of known classes, leaving more low-density regions for unknown classes; UPL optimizes unknown probability based on the uncertainty of predictions, which further divides more low-density regions around the cluster of known classes. Thus, unknown objects in low-density regions can be easily identified with the learned unknown probability. Extensive experiments demonstrate that our method can significantly improve the OSOD performance, e.g., OpenDet reduces the Absolute Open-Set Errors by 25%-35% on six OSOD benchmarks. Code is available at: https://github.com/csuhan/opendet2.