# Open-set object detection: towards unified problem formulation and benchmarking

arXiv: https://arxiv.org/abs/2411.05564 · HTML: https://mlsanity.com/paper/2411.05564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)
- [VOS: Learning What You Don't Know by Virtual Outlier Synthesis](https://mlsanity.com/paper/2202.01197/) (2022)
- [Mitigating Neural Network Overconfidence with Logit Normalization](https://mlsanity.com/paper/2205.09310/) (2022)
- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [Rethinking Open-Set Object Detection: Issues, a New Formulation, and Taxonomy](https://mlsanity.com/paper/2207.09775/) (2022)

## Built on by (most cited first, 4 of 4)

- [Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception](https://mlsanity.com/paper/2509.09297/) (2025)
- [FSP-DETR: Few-Shot Prototypical Parasitic Ova Detection](https://mlsanity.com/paper/2510.09583/) (2025)
- [FindMeIfYouCan: Bringing Open Set metrics to textitnear ,  textitfar  and textitfarther Out-of-Distribution Object Detection](https://mlsanity.com/paper/2506.14008/) (2025)
- [Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning](https://mlsanity.com/paper/2603.16122/) (2026)

## Abstract (arXiv)

In real-world applications where confidence is key, like autonomous driving, the accurate detection and appropriate handling of classes differing from those used during training are crucial. Despite the proposal of various unknown object detection approaches, we have observed widespread inconsistencies among them regarding the datasets, metrics, and scenarios used, alongside a notable absence of a clear definition for unknown objects, which hampers meaningful evaluation. To counter these issues, we introduce two benchmarks: a unified VOC-COCO evaluation, and the new OpenImagesRoad benchmark which provides clear hierarchical object definition besides new evaluation metrics. Complementing the benchmark, we exploit recent self-supervised Vision Transformers performance, to improve pseudo-labeling-based OpenSet Object Detection (OSOD), through OW-DETR++. State-of-the-art methods are extensively evaluated on the proposed benchmarks. This study provides a clear problem definition, ensures consistent evaluations, and draws new conclusions about effectiveness of OSOD strategies.