RT-SDGOD: Real-Time Single-Domain Generalized Object Detection

• URL: http://arxiv.org/abs/2606.09367v1
• Date: Mon, 08 Jun 2026 11:39:18 GMT
• Title: RT-SDGOD: Real-Time Single-Domain Generalized Object Detection
• Abstract summary: Under real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors.<n>We introduce Real-Time Single-Domain Generalized Object Detection (RT-SDGOD)<n>Our method achieves better generalization performance than existing approaches across multiple unseen target domains.
• Score: 29.73970506841414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world deployment under strict real-time constraints, weather and imaging variations induce significant distribution shifts, severely degrading detectors. Single-Domain Generalized Object Detection aims to mitigate this issue, yet existing methods rarely investigate-at the level of problem formulation-the generalization capability of real-time detectors under such constrained inference budgets. To this end, we introduce Real-Time Single-Domain Generalized Object Detection (RT-SDGOD), which focuses on how real-time detectors can achieve cross-domain generalization under zero extra inference overhead by relying solely on training-time representation learning. We observe that, under domain shift, DETR-based real-time detectors mainly degrade through increased missed detections, rooted in limited and unstable object-level discriminative evidence. Based on this, we propose RT-SDGDet, a multi-evidence collaborative modeling framework for RT-SDGOD. The core idea is to enable multiple queries of the same object to collaboratively cover more sufficient discriminative evidence while maintaining the stability of such evidence modeling across views. Specifically, we use one-to-many (O2M) supervision to construct stable object-specific query groups, and further design Discriminative Evidence Diversity Learning (DEDL) and Dual-view Evidence Consistency Learning (DvECL) to expand object-level evidence coverage and improve evidence stability under appearance perturbations, respectively. Since all components are introduced only during training, our method incurs no extra inference overhead. Extensive experiments show that the proposed method achieves better generalization performance than existing approaches across multiple unseen target domains.

Related papers

• VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection [33.39250067795076]
  In real-world scenarios, continual changes in weather, illumination, and imaging conditions cause significant domain shifts.<n>We propose a dual-prior learning framework for generalized object detection (SDGOD)<n>In the encoding stage, we propose Cross-domain Stable Prior Distillation to enhance the robustness of object-background and inter-instance modeling.<n>In the decoding stage, we propose Semantic-Contextual Prior-based Query Enhancement.
  arXiv  Detail & Related papers  (2026-04-23T10:04:36Z)
• Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain [26.03122882760887]
  We propose a spatial-spectral collaborative perception network for cross-domain IRSTD.<n>We develop a phase rectification module (PRM) to derive generalizable target awareness.<n>We employ an attention mechanism (OAM) in skip connections to preserve positional information.
  arXiv  Detail & Related papers  (2026-04-02T11:54:28Z)
• Integrating Diverse Assignment Strategies into DETRs [61.61489761918158]
  Label assignment is a critical component in object detectors, particularly within DETR-style frameworks.<n>We propose LoRA-DETR, a flexible and lightweight framework that seamlessly integrates diverse assignment strategies into any DETR-style detector.
  arXiv  Detail & Related papers  (2026-01-14T07:28:54Z)
• RAAR: Retrieval Augmented Agentic Reasoning for Cross-Domain Misinformation Detection [31.765619582229146]
  RAAR is a retrieval-augmented agentic reasoning framework for cross-domain misinformation detection.<n> RAAR retrieves source-domain evidence aligned with each target sample's semantics, sentiment, and writing style.<n> RAAR constructs verifiable multi-step reasoning paths through specialized multi-agent collaboration.
  arXiv  Detail & Related papers  (2026-01-08T11:43:16Z)
• DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic [12.91797400491484]
  Real-world object detection systems, such as those in autonomous driving and surveillance, must continuously learn new object categories.<n>Existing approaches, Class Incremental Object Detection (CIOD) and Domain Incremental Object Detection (DIOD) only address one aspect of this challenge.<n>We propose Dual Incremental Object Detection (DuIOD), a more practical setting that simultaneously handles class and domain shifts in an exemplar-free manner.
  arXiv  Detail & Related papers  (2025-06-26T13:41:47Z)
• Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks [49.84182981950623]
  Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.<n>It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies.<n>We introduce an efficient and easily deployable multispectral object detection framework that can seamlessly optimize high-performing single-modality models.
  arXiv  Detail & Related papers  (2024-11-27T12:18:39Z)
• Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector [72.05791402494727]
  This paper studies the challenging cross-domain few-shot object detection (CD-FSOD) It aims to develop an accurate object detector for novel domains with minimal labeled examples.
  arXiv  Detail & Related papers  (2024-02-05T15:25:32Z)
• Towards Domain Generalization in Object Detection [32.68332102237882]
  We study the critical problem, domain generalization in object detection (DGOD), where detectors are trained with source domains and evaluated on unknown target domains. We propose a novel method named Region Aware Proposal reweighTing (RAPT) to eliminate dependence within RoI features.
  arXiv  Detail & Related papers  (2022-03-27T20:35:37Z)
• Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement [79.2994130944482]
  We design a Domain Disentanglement Faster-RCNN (DDF) to eliminate the source-specific information in the features for detection task learning. Our DDF method facilitates the feature disentanglement at the global and local stages, with a Global Triplet Disentanglement (GTD) module and an Instance Similarity Disentanglement (ISD) module. By outperforming state-of-the-art methods on four benchmark UDA object detection tasks, our DDF method is demonstrated to be effective with wide applicability.
  arXiv  Detail & Related papers  (2022-01-06T05:43:01Z)
• Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency [90.71745178767203]
  Deep learning-based 3D object detection has achieved unprecedented success with the advent of large-scale autonomous driving datasets. Existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world. We study a more realistic setting, unsupervised 3D domain adaptive detection, which only utilizes source domain annotations.
  arXiv  Detail & Related papers  (2021-07-23T17:19:23Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)

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.