Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision

• URL: http://arxiv.org/abs/2311.14758v2
• Date: Thu, 21 Mar 2024 12:43:32 GMT
• Title: Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision
• Authors: Yi Yu, Xue Yang, Qingyun Li, Feipeng Da, Jifeng Dai, Yu Qiao, Junchi Yan,
• Abstract summary: 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.
• Score: 81.60564776995682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning rotated box (RBox) from the horizontal box (HBox) has attracted more and more attention. In this paper, we explore a more challenging yet label-efficient setting, namely single point-supervised OOD, and present our approach called Point2RBox. Specifically, we propose to leverage two principles: 1) Synthetic pattern knowledge combination: By sampling around each labeled point on the image, we spread the object feature to synthetic visual patterns with known boxes to provide the knowledge for box regression. 2) Transform self-supervision: With a transformed input image (e.g. scaled/rotated), the output RBoxes are trained to follow the same transformation so that the network can perceive the relative size/rotation between objects. The detector is further enhanced by a few devised techniques to cope with peripheral issues, e.g. the anchor/layer assignment as the size of the object is not available in our point supervision setting. To our best knowledge, Point2RBox is the first end-to-end solution for point-supervised OOD. In particular, our method uses a lightweight paradigm, yet it achieves a competitive performance among point-supervised alternatives, 41.05%/27.62%/80.01% on DOTA/DIOR/HRSC datasets.

Related papers

• PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation [32.04698431036215]
  In this paper, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage the spatial awareness and precise supervision offered by these two methods to address their respective limitations.
  arXiv  Detail & Related papers  (2024-11-09T02:38:29Z)
• PointOBB: Learning Oriented Object Detection via Single Point Supervision [55.88982271340328]
  This paper proposes PointOBB, the first single Point-based OBB generation method, for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view, a resized view, and a rotated/flipped (rot/flp) view. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance.
  arXiv  Detail & Related papers  (2023-11-23T15:51:50Z)
• P2RBox: Point Prompt Oriented Object Detection with SAM [28.96914721062631]
  We introduce P2RBox, which employs point prompt to generate rotated box (RBox) annotation for oriented object detection. P2RBox incorporates two advanced guidance cues: Boundary Sensitive Mask guidance, and Centrality guidance, which utilize spatial information to reduce granularity ambiguity. Compared to the state-of-the-art point-annotated generative method PointOBB, P2RBox outperforms by about 29% mAP on DOTA-v1.0 dataset.
  arXiv  Detail & Related papers  (2023-11-22T03:33:00Z)
• 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-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection [55.3948651109885]
  We present H2RBox-v2, to bridge the gap between HBox-supervised and RBox-supervised oriented object detection. To our best knowledge, H2RBox-v2 is the first symmetry-aware self-supervised paradigm for oriented object detection.
  arXiv  Detail & Related papers  (2023-04-10T06:11:40Z)
• Anchor Free remote sensing detector based on solving discrete polar coordinate equation [4.708085033897991]
  We propose an Anchor Free aviatic remote sensing object detector (BWP-Det) to detect rotating and multi-scale object. Specifically, we design a interactive double-branch(IDB) up-sampling network, in which one branch gradually up-sampling is used for the prediction of Heatmap. We improve a weighted multi-scale convolution (WmConv) in order to highlight the difference between foreground and background.
  arXiv  Detail & Related papers  (2023-03-21T09:28:47Z)
• 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)
• Point-to-Box Network for Accurate Object Detection via Single Point Supervision [51.95993495703855]
  We introduce a lightweight alternative to the off-the-shelf proposal (OTSP) method. P2BNet can construct an inter-objects balanced proposal bag by generating proposals in an anchor-like way. The code will be released at COCO.com/ucas-vg/P2BNet.
  arXiv  Detail & Related papers  (2022-07-14T11:32:00Z)

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.