Towards Sparsely Annotated Open-World Object Detection

2026-08-12

AI Summary

This work addresses the challenge of object detection in real-world scenarios where sparse annotations and unknown object categories coexist, leading to ambiguous supervision that hinders differentiation between unlabeled known objects and truly novel ones. To tackle this issue, the paper introduces Sparse Annotation Open-World Object Detection (SA-OWOD), a new task formulation, and proposes DPOD, a unified framework comprising a Known Target Recovery Module (KTRM) that reconstructs complete supervision signals and regularizes the feature space, and a Dual-view Discrepant Target Generator (DDTG) that leverages cross-view semantic discrepancies to identify reliable unknown objects. Evaluated on a sparse-annotation open-world benchmark, DPOD significantly outperforms existing methods, achieving notable gains especially in unknown object detection performance.

Abstract

Real-world object detection operates under ambiguous supervision, where unlabeled regions may correspond to missing annotations of known objects or genuinely unknown categories. These challenges have been addressed separately in Sparsely Annotated Object Detection (SAOD) and Open-World Object Detection (OWOD). In practice, their co-occurrence remains an open problem. To address this problem, we introduce Sparsely Annotated Open-World Object Detection (SA-OWOD), a new task that jointly considers sparse supervision and the presence of unseen categories. We propose Dual-Perspective Object Discovery (DPOD), a unified framework that jointly models unlabeled known and unknown instances via two complementary mechanisms. The Known Target Recovery Module (KTRM) recovers supervision for unlabeled known instances and explicitly regularizes the feature space to separate known and unknown representations. Complementarily, the Dual-Disagreement Target Generator (DDTG) identifies reliable unknown candidates through cross-view semantic inconsistency. By integrating these modules, DPOD resolves contradictory supervision signals caused by ambiguous unlabeled regions. As a result, it prevents misclassification between known and unknown objects and stabilizes the decision boundaries. Experimental results on sparsely annotated open-world benchmarks demonstrate that the proposed method outperforms existing open-world detection methods, particularly in detecting unknown objects.

Problem

• Sparsely Annotated Object Detection
• Open-World Object Detection
• Ambiguous Supervision
• Unknown Categories
• Sparse Annotation

Innovation

• Sparsely Annotated Object Detection
• Open-World Object Detection
• Dual-Perspective Object Discovery
• Unknown Object Detection
• Ambiguous Supervision