Foundation-Assisted Active Learning for Object Detection Annotation

• URL: http://arxiv.org/abs/2607.16671v1
• Date: Sat, 18 Jul 2026 06:59:08 GMT
• Title: Foundation-Assisted Active Learning for Object Detection Annotation
• Abstract summary: We propose a foundation-model-collaborative active learning and semi-automatic annotation framework for remote sensing object detection.<n>We build a dual-source mechanism consisting of a reference localization source (SA-source) based on UPN+SAM2 and a detector prediction source (OD-source)<n>We also propose Object-Centric Diversity Sampling, which constructs object-level representations using DINOv2 features and SAM2 masks to improve sample coverage while suppressing pseudo-diversity.
• Score: 25.84959945001749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The annotation cost for remote sensing object detection is high, while existing active learning methods still face several challenges in object detection scenarios, including the coupling of localization and classification uncertainty, severe localization noise in the cold-start stage, and pseudo-diversity caused by high-recall candidate proposals. To address these issues, we propose a foundation-model-collaborative active learning and semi-automatic annotation framework for efficient construction of remote sensing object detection datasets. We build a dual-source mechanism consisting of a reference localization source (SA-source) based on UPN+SAM2 and a detector prediction source (OD-source), and further propose a Foundation-model-enhanced Dual-Source Uncertainty estimation to improve sample selection quality in the cold-start stage by jointly modeling localization consistency and classification confidence. Furthermore, we propose Object-Centric Diversity Sampling, which constructs object-level representations using DINOv2 features and SAM2 masks to improve sample coverage while suppressing pseudo-diversity. To address geometric noise in the semi-automatic annotation stage, we design Dual-Source Box Switching, which replaces noisy detector boxes with matched refined boxes from the SA-source, thereby reducing the manual burden of box refinement. Experiments on DIOR, HRSC2016, DOTAv2, and FAIR1M show that our method achieves superior or comparable results under most annotation budgets, with notably stronger cold-start sample efficiency in the low-budget regime.

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)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• Reliable Few-shot Learning under Dual Noises [166.53173694689693]
  We propose DEnoised Task Adaptation (DETA++) for reliable few-shot learning.<n>DETA++ employs a memory bank to store and refine clean regions for each inner-task class, based on which a Local Nearestid (LocalNCC) is devised to yield noise-robust predictions on query samples.<n>Extensive experiments demonstrate the effectiveness and flexibility of DETA++.
  arXiv  Detail & Related papers  (2025-06-19T14:05:57Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Multi-Source Domain Adaptation for Object Detection [52.87890831055648]
  We propose a unified Faster R-CNN based framework, termed Divide-and-Merge Spindle Network (DMSN) DMSN can simultaneously enhance domain innative and preserve discriminative power. We develop a novel pseudo learning algorithm to approximate optimal parameters of pseudo target subset.
  arXiv  Detail & Related papers  (2021-06-30T03:17:20Z)
• Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance [0.0]
  This study presents an enhanced 2D object detector based on Faster R-CNN that is better suited for the context of autonomous vehicles. The proposed modifications over the Faster R-CNN do not increase computational cost and can easily be extended to optimize other anchor-based detection frameworks.
  arXiv  Detail & Related papers  (2021-04-08T16:58:31Z)
• ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification [25.035093667770052]
  We propose an Anti-Noise Learning (ANL) approach, which contains two modules. FDA module is designed to gather the id-related samples and disperse id-unrelated samples, through the camera-wise contrastive learning and adversarial adaptation. Reliable Sample Selection ( RSS) module utilizes an Auxiliary Model to correct noisy labels and select reliable samples for the Main Model.
  arXiv  Detail & Related papers  (2020-12-27T02:38:45Z)
• AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection [8.39479809973967]
  Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce examples. Existing methods solve this problem by performing subtasks of classification and localization utilizing a shared component. We present that a general few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations.
  arXiv  Detail & Related papers  (2020-11-30T10:21:32Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

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.