Detecting Unknown Objects via Energy-based Separation for Open World Object Detection

• URL: http://arxiv.org/abs/2603.29954v1
• Date: Tue, 31 Mar 2026 16:20:56 GMT
• Title: Detecting Unknown Objects via Energy-based Separation for Open World Object Detection
• Abstract summary: We propose DEUS (Detecting Unknowns via energy-based Separation), a novel framework that addresses the challenges of Open World Object Detection.<n>Deus consists of Equiangular Tight Frame (ETF)-Subspace Unknown Separation (EUS) and an Energy-based Known Distinction (EKD) loss.<n>We thoroughly validate DEUS on OWOD benchmarks, demonstrating outstanding performance improvements in unknown detection while maintaining competitive known class performance.
• Score: 20.39851978931329
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we tackle the problem of Open World Object Detection (OWOD). This challenging scenario requires the detector to incrementally learn to classify known objects without forgetting while identifying unknown objects without supervision. Previous OWOD methods have enhanced the unknown discovery process and employed memory replay to mitigate catastrophic forgetting. However, since existing methods heavily rely on the detector's known class predictions for detecting unknown objects, they struggle to effectively learn and recognize unknown object representations. Moreover, while memory replay mitigates forgetting of old classes, it often sacrifices the knowledge of newly learned classes. To resolve these limitations, we propose DEUS (Detecting Unknowns via energy-based Separation), a novel framework that addresses the challenges of Open World Object Detection. DEUS consists of Equiangular Tight Frame (ETF)-Subspace Unknown Separation (EUS) and an Energy-based Known Distinction (EKD) loss. EUS leverages ETF-based geometric properties to create orthogonal subspaces, enabling cleaner separation between known and unknown object representations. Unlike prior energy-based approaches that consider only the known space, EUS utilizes energies from both spaces to better capture distinct patterns of unknown objects. Furthermore, EKD loss enforces the separation between previous and current classifiers, thus minimizing knowledge interference between previous and newly learned classes during memory replay. We thoroughly validate DEUS on OWOD benchmarks, demonstrating outstanding performance improvements in unknown detection while maintaining competitive known class performance.

Related papers

• REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling [56.888597798431704]
  Open-World Object Detection (OWOD) requires detectors to identify previously unseen objects as unknown and incrementally incorporate them into the set of known categories.<n>Existing frameworks rely heavily on exemplar replay to mitigate catastrophic forgetting, but in some real applications, storing raw data conflicts with data access restrictions and leads to data exposure risks.<n>We propose REAL-OW, a novel rehearsal-free framework that decouples incremental knowledge through a collaborative adapter architecture.
  arXiv  Detail & Related papers  (2026-07-03T06:38:54Z)
• Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model [51.81962097623522]
  Open-world object detection (OWOD) requires incrementally detecting known categories while reliably identifying unknown objects.<n>This paper aims to make the entire OWOD framework interpretable, enabling the detector to truly "knowing the unknown"
  arXiv  Detail & Related papers  (2026-02-24T07:08:47Z)
• UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework [13.310007077914122]
  We tackle the problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images.<n>We propose UADet, an Uncertainty-Aware Open-Set Object Detector that considers appearance and geometric uncertainty.
  arXiv  Detail & Related papers  (2024-12-12T12:38:33Z)
• Semi-supervised Open-World Object Detection [74.95267079505145]
  We introduce a more realistic formulation, named semi-supervised open-world detection (SS-OWOD) We demonstrate that the performance of the state-of-the-art OWOD detector dramatically deteriorates in the proposed SS-OWOD setting. Our experiments on 4 datasets including MS COCO, PASCAL, Objects365 and DOTA demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-02-25T07:12:51Z)
• Unsupervised Recognition of Unknown Objects for Open-World Object Detection [28.787586991713535]
  Open-World Object Detection (OWOD) extends object detection problem to a realistic and dynamic scenario. Current OWOD models, such as ORE and OW-DETR, focus on pseudo-labeling regions with high objectness scores as unknowns. This paper proposes a novel approach that learns an unsupervised discriminative model to recognize true unknown objects.
  arXiv  Detail & Related papers  (2023-08-31T08:17:29Z)
• Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects [21.426594215463105]
  We propose the unknown sniffer (UnSniffer) to find both unknown and known objects. GOC score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. We present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge.
  arXiv  Detail & Related papers  (2023-03-24T02:59:44Z)
• Open World DETR: Transformer based Open World Object Detection [60.64535309016623]
  We propose a two-stage training approach named Open World DETR for open world object detection based on Deformable DETR. We fine-tune the class-specific components of the model with a multi-view self-labeling strategy and a consistency constraint. Our proposed method outperforms other state-of-the-art open world object detection methods by a large margin.
  arXiv  Detail & Related papers  (2022-12-06T13:39:30Z)
• More Practical Scenario of Open-set Object Detection: Open at Category Level and Closed at Super-category Level [23.98839374194848]
  Open-set object detection (OSOD) has recently attracted considerable attention. We first point out that the scenario of OSOD considered in recent studies, which considers an unlimited variety of unknown objects, has a fundamental issue. This issue leads to difficulty with the evaluation of methods' performance on unknown object detection.
  arXiv  Detail & Related papers  (2022-07-20T09:28:51Z)
• Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild [29.155394550694197]
  We propose a new unknown-aware object detection framework through Spatial-Temporal Unknown Distillation (STUD) STUD distills unknown objects from videos in the wild and meaningfully regularizes the model's decision boundary. We establish the state-of-the-art performance on OOD detection tasks for object detection, reducing the FPR95 score by over 10% compared to the previous best method.
  arXiv  Detail & Related papers  (2022-03-08T01:44:03Z)
• OW-DETR: Open-world Detection Transformer [90.56239673123804]
  We introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification and objectness scoring. Our model outperforms the recently introduced OWOD approach, ORE, with absolute gains ranging from 1.8% to 3.3% in terms of unknown recall.
  arXiv  Detail & Related papers  (2021-12-02T18:58:30Z)
• Towards Open World Object Detection [68.79678648726416]
  ORE: Open World Object Detector is based on contrastive clustering and energy based unknown identification. We find that identifying and characterizing unknown instances helps to reduce confusion in an incremental object detection setting.
  arXiv  Detail & Related papers  (2021-03-03T18:58:18Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.