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ARXIV:2603.24166 · REFERRING OBJECT DETECTION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24166REFERRING OBJECT DETECTIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEXu Zhang · Zhe Chen · Jing Zhang · Dacheng Tao · arXiv
A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments.
Opportunity summary
Pain A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting…
Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity.…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By biasing both training and inference toward plausible candidates, these priors promise to improve label efficiency and convergence performance. Code availability is flagged in…
Referring Object Detection moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments.
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10.48550/arXiv.2603.24166A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments.
Abstract
Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting precious samples. We ask a simple question: Can explicit reasoning priors help models learn more efficiently when data is scarce? To explore this, we first introduce a Data-efficient Referring Object Detection (De-ROD) task, which is a benchmark protocol for measuring ROD performance in low-data and few-shot settings. We then propose the HeROD (Heuristic-inspired ROD), a lightweight, model-agnostic framework that injects explicit, heuristic-inspired spatial and semantic reasoning priors, which are interpretable signals derived based on the referring phrase, into 3 stages of a modern DETR-style pipeline: proposal ranking, prediction fusion, and Hungarian matching. By biasing both training and inference toward plausible candidates, these priors promise to improve label efficiency and convergence performance. On RefCOCO, RefCOCO+, and RefCOCOg, HeROD consistently outperforms strong grounding baselines in scarce-label regimes. More broadly, our results suggest that integrating simple, interpretable reasoning priors provides a practical and extensible path toward better data-efficient vision-language understanding.
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PROBLEM
A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting precious samples.
METHOD
Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity. In such regimes,...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By biasing both training and inference toward plausible candidates, these priors promise to improve label efficiency and convergence performance. Code availability is flagged in the production record; the...
WHY NOW
Referring Object Detection moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting precious samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting precious samples.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. By biasing both training and inference toward plausible candidates, these priors promise to improve label efficiency and convergence performance. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Referring Object Detection moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework that injects heuristic reasoning priors to improve data efficiency in referring object detection for low-data environments.
Segment
Referring Object Detection
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