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ARXIV:2605.10349 · OBJECT DETECTION · SUBMITTED 12 MAY · 20:14 UTC · FRESHNESS FRESH
ARXIV:2605.10349OBJECT DETECTIONSUBMITTED 12 MAY · 20:14 UTCFRESHNESS FRESHRashi Sharma · Justin Timothy C. Bersamin · Karthikk Subramanian · arXiv
A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead.
Opportunity summary
Pain A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead. This challenge is compounded by the need to preserve high accuracy while minimizing…
Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications.
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on COCO, PASCAL VOC, and BDD100K demonstrate that PAL consistently improves label efficiency and detection accuracy compared to existing active learning baselines,…
Object Detection moved forward this cycle; last verified May 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead.
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10.48550/arXiv.2605.10349A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead.
Abstract
Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications. Prior active learning methods often depend on model features or modify detector internals and training schedules, increasing integration overhead. Moreover, they rarely jointly exploit the benefits of image-level signals, class-imbalance cues, and instance-level uncertainty for comprehensive selection. We present Portable Active Learning (PAL), a detector-agnostic, easily portable framework that operates solely on inference outputs. PAL combines class-wise instance uncertainty with image-level diversity to guide data selection. At each round, PAL trains lightweight class-specific logistic classifiers to distinguish true from false positives, producing entropy-based uncertainty scores for proposals. Candidate images are then refined using global image entropy, class diversity, and image similarity, yielding batches that are both informative and diverse. PAL requires no changes to model internals or training pipelines, ensuring broad compatibility across detectors. Extensive experiments on COCO, PASCAL VOC, and BDD100K demonstrate that PAL consistently improves label efficiency and detection accuracy compared to existing active learning baselines, making it a practical solution for scalable and cost-effective deployment of object detection in real-world settings.
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Dimensions overall score 8.0
PROBLEM
A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-...
METHOD
Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on COCO, PASCAL VOC, and BDD100K demonstrate that PAL consistently improves label efficiency and detection accuracy compared to existing active learning baselines, making it a practi...
WHY NOW
Object Detection moved forward this cycle; last verified May 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Annotating bounding boxes is costly and limits the scalability of object detection. This challenge is compounded by the need to preserve high accuracy while minimizing manual effort in real-world applications.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on COCO, PASCAL VOC, and BDD100K demonstrate that PAL consistently improves label efficiency and detection accuracy compared to existing active learning baselines, making it a practical solution for scalable and cost-effective deployment of object detection in real-world settings. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Object Detection moved forward this cycle; last verified May 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A portable, detector-agnostic framework for active learning in object detection that significantly improves label efficiency and accuracy with minimal integration overhead.
Segment
Object Detection
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Commercial read
8.0/10 public viability
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Technical feasibility
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Run minimal reproduction from the Build Passport prototype path.
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DEFENSIBILITY
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