Partial Weakly-Supervised Oriented Object Detection

2025-07-03

AI Summary

To address the high annotation cost of oriented object detection (OOD) under weak supervision, this paper proposes PWOOD, a semi-weakly supervised framework that leverages only partial weak annotations—such as axis-aligned bounding boxes or single points—together with abundant unlabeled data. Methodologically, PWOOD introduces an OS-Student model that explicitly encodes orientation and scale information, and incorporates a class-agnostic pseudo-label filtering (CPF) strategy to eliminate reliance on fixed confidence thresholds. It adopts a student–teacher paradigm integrating consistency regularization and dynamic pseudo-label refinement. On DOTA and DIOR benchmarks, PWOOD achieves performance comparable to or surpassing state-of-the-art semi-supervised methods, while requiring significantly less annotation effort than existing weakly supervised approaches. To our knowledge, PWOOD is the first work to systematically establish a partial weak supervision paradigm for OOD, bridging the gap between semi-supervised learning and practical annotation constraints in remote sensing and aerial imagery analysis.

Technology Category

• Machine Learning: Semi-Supervised Learning
• Computer Vision: Object Detection & Categorization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The growing demand for oriented object detection (OOD) across various domains has driven significant research in this area. However, the high cost of dataset annotation remains a major concern. Current mainstream OOD algorithms can be mainly categorized into three types: (1) fully supervised methods using complete oriented bounding box (OBB) annotations, (2) semi-supervised methods using partial OBB annotations, and (3) weakly supervised methods using weak annotations such as horizontal boxes or points. However, these algorithms inevitably increase the cost of models in terms of annotation speed or annotation cost. To address this issue, we propose:(1) the first Partial Weakly-Supervised Oriented Object Detection (PWOOD) framework based on partially weak annotations (horizontal boxes or single points), which can efficiently leverage large amounts of unlabeled data, significantly outperforming weakly supervised algorithms trained with partially weak annotations, also offers a lower cost solution; (2) Orientation-and-Scale-aware Student (OS-Student) model capable of learning orientation and scale information with only a small amount of orientation-agnostic or scale-agnostic weak annotations; and (3) Class-Agnostic Pseudo-Label Filtering strategy (CPF) to reduce the model's sensitivity to static filtering thresholds. Comprehensive experiments on DOTA-v1.0/v1.5/v2.0 and DIOR datasets demonstrate that our PWOOD framework performs comparably to, or even surpasses, traditional semi-supervised algorithms.

Problem

• Reducing annotation cost in oriented object detection
• Leveraging weak annotations for efficient model training
• Improving detection accuracy with partial weak supervision

Innovation

• Partial Weakly-Supervised OOD framework
• OS-Student model for weak annotations
• Class-Agnostic Pseudo-Label Filtering strategy