Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection

• URL: http://arxiv.org/abs/2502.09471v1
• Date: Thu, 13 Feb 2025 16:34:59 GMT
• Title: Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for Weakly-supervised Oriented Object Detection
• Authors: Yi Yu, Xue Yang, Yansheng Li, Zhenjun Han, Feipeng Da, Junchi Yan,
• Abstract summary: Wholly-WOOD is a weakly-supervised OOD framework capable of wholly leveraging various labeling forms. By only using HBox for training, our Wholly-WOOD achieves performance very close to that of the RBox-trained counterpart on remote sensing.
• Score: 57.26265276035267
• License:
• Abstract: Accurately estimating the orientation of visual objects with compact rotated bounding boxes (RBoxes) has become a prominent demand, which challenges existing object detection paradigms that only use horizontal bounding boxes (HBoxes). To equip the detectors with orientation awareness, supervised regression/classification modules have been introduced at the high cost of rotation annotation. Meanwhile, some existing datasets with oriented objects are already annotated with horizontal boxes or even single points. It becomes attractive yet remains open for effectively utilizing weaker single point and horizontal annotations to train an oriented object detector (OOD). We develop Wholly-WOOD, a weakly-supervised OOD framework, capable of wholly leveraging various labeling forms (Points, HBoxes, RBoxes, and their combination) in a unified fashion. By only using HBox for training, our Wholly-WOOD achieves performance very close to that of the RBox-trained counterpart on remote sensing and other areas, significantly reducing the tedious efforts on labor-intensive annotation for oriented objects. The source codes are available at https://github.com/VisionXLab/whollywood (PyTorch-based) and https://github.com/VisionXLab/whollywood-jittor (Jittor-based).

Related papers

• Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances [50.80161958767447]
  We present Point2RBox-v2, an approach to explore the spatial layout among instances for learning point-supervised OOD. Our solution is elegant and lightweight, yet it is expected to give a competitive performance especially in densely packed scenes.
  arXiv  Detail & Related papers  (2025-02-06T18:07:25Z)
• Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation [58.37525311718006]
  We put forth a novel formulation of the aerial object detection problem, namely open-vocabulary aerial object detection (OVAD) We propose CastDet, a CLIP-activated student-teacher detection framework that serves as the first OVAD detector specifically designed for the challenging aerial scenario. Our framework integrates a robust localization teacher along with several box selection strategies to generate high-quality proposals for novel objects.
  arXiv  Detail & Related papers  (2024-11-04T12:59:13Z)
• Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision [81.60564776995682]
  We present Point2RBox, an end-to-end solution for point-supervised object detection. Our method uses a lightweight paradigm, yet it achieves a competitive performance among point-supervised alternatives. In particular, our method uses a lightweight paradigm, yet it achieves a competitive performance among point-supervised alternatives.
  arXiv  Detail & Related papers  (2023-11-23T15:57:41Z)
• SOOD: Towards Semi-Supervised Oriented Object Detection [57.05141794402972]
  This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark.
  arXiv  Detail & Related papers  (2023-04-10T11:10:42Z)
• H2RBox: Horizonal Box Annotation is All You Need for Oriented Object Detection [63.66553556240689]
  Oriented object detection emerges in many applications from aerial images to autonomous driving. Many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box. This paper proposes a simple yet effective oriented object detection approach called H2RBox.
  arXiv  Detail & Related papers  (2022-10-13T05:12:45Z)
• Oriented Bounding Boxes for Small and Freely Rotated Objects [7.6997148655751895]
  A novel object detection method is presented that handles freely rotated objects of arbitrary sizes. The method encodes the precise location and orientation of features of the target objects at grid cell locations. Evaluations on the xView and DOTA datasets show that the proposed method uniformly improves performance over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-24T02:04:49Z)
• Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors [21.484827209503823]
  Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions. Current oriented object detection methods mainly rely on two-stage anchor-based detectors. We extend the horizontal keypoint-based object to the oriented object detection task.
  arXiv  Detail & Related papers  (2020-08-17T00:56:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.