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Partial Weakly-Supervised Oriented Object Detection

Mingxin Liu, Peiyuan Zhang, Yuan Liu, Wei Zhang, Yue Zhou, Ning Liao, Ziyang Gong, Junwei Luo, Zhirui Wang, Yi Yu, Xue Yang

TL;DR

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.

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. Our code will be made publicly available.

Partial Weakly-Supervised Oriented Object Detection

TL;DR

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.

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. Our code will be made publicly available.

Paper Structure

This paper contains 13 sections, 8 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. PWOOD Framework
  5. OS-Student
  6. Class-Agnostic Pseudo-Label Filtering
  7. Overall Loss
  8. Experiment
  9. Datasets and Experimental Setting
  10. Main Results
  11. Ablation Study
  12. More Disscussion
  13. Conclusion

Figures (5)

  • Figure 1: The main paradigmatic types of existing oriented object detection. Compared to these settings, our proposed Partial Weakly-supervised PWOOD offers high efficiency, low costs, and good performance (accuracy on DOTA-v1.0 is shown).
  • Figure 2: The overview of the proposed PWOOD framework. Orientation Learning and Scale Learning modules enables the Orientation-and-Scale-aware Student to learn both scale and orientation information from weakly annotated data, as well as Class-Agnostic Pseudo-Label Filtering mechanism enhances pseudo-label quality by leveraging dynamic thresholds.
  • Figure 3: Ablation of pseudo-label filtering strategies.
  • Figure 4: Visualized performance comparison of PWOOD with H2RBox-v2 and the Vanilla Baseline.
  • Figure 5: The preliminary experimental results of joint training with various annotations on DOTA-v1.5 Dataset-Partial