Object Localization under Single Coarse Point Supervision

• URL: http://arxiv.org/abs/2203.09338v1
• Date: Thu, 17 Mar 2022 14:14:11 GMT
• Title: Object Localization under Single Coarse Point Supervision
• Authors: Xuehui Yu, Pengfei Chen, Di Wu, Najmul Hassan, Guorong Li, Junchi Yan, Humphrey Shi, Qixiang Ye, Zhenjun Han
• Abstract summary: We propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL) In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision.
• Score: 107.46800858130658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic variance for the inconsistency of annotated points. Existing POL methods heavily reply on accurate key-point annotations which are difficult to define. In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. To this end, we propose a coarse point refinement (CPR) approach, which to our best knowledge is the first attempt to alleviate semantic variance from the perspective of algorithm. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL). In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision. Experimental results on COCO, DOTA and our proposed SeaPerson dataset validate the effectiveness of the CPR approach. The dataset and code will be available at https://github.com/ucas-vg/PointTinyBenchmark/.

Related papers

• Dense Center-Direction Regression for Object Counting and Localization with Point Supervision [1.9526430269580954]
  We propose a novel approach termed CeDiRNet for point-supervised learning. It uses a dense regression of directions pointing towards the nearest object centers. We show that it outperforms the existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-26T17:49:27Z)
• CPR++: Object Localization via Single Coarse Point Supervision [55.8671776333499]
  coarse point refinement (CPR) is first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. CPR++ can obtain scale information and further reduce the semantic variance in a global region.
  arXiv  Detail & Related papers  (2024-01-30T17:38:48Z)
• Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysis [59.12922158979068]
  Recently-based methods have shown strong performance in point cloud analysis. Simple architectures are able to learn geometric features in local point groups yet fail to model long-range dependencies directly. We propose Point Deformable Network (PDNet) to capture long-range relations with strong representation ability.
  arXiv  Detail & Related papers  (2023-12-20T14:52:07Z)
• SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data [17.471342278936365]
  We propose a new method to infer keypoints from arbitrary object categories in practical scenarios where point cloud data (PCD) are noisy, down-sampled and arbitrarily rotated. We achieve these desiderata by proposing a new self-supervised training strategy for keypoints estimation. We compare the keypoints estimated by the proposed approach with those of the state-of-the-art unsupervised approaches.
  arXiv  Detail & Related papers  (2023-08-10T08:10:01Z)
• Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations [81.02347863372364]
  We present Point-Teaching, a weakly semi-supervised object detection framework. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We propose a simple-yet-effective data augmentation, termed point-guided copy-paste, to reduce the impact of the unmatched points.
  arXiv  Detail & Related papers  (2022-06-01T07:04:38Z)
• A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds [54.73161039445703]
  We propose a novel self-training approach that enables a typical object detector trained only with point-level annotations. During training, we utilize the available point annotations to supervise the estimation of the center points of objects. Experimental results show that our approach significantly outperforms state-of-the-art point-supervised methods under both detection and counting tasks.
  arXiv  Detail & Related papers  (2020-07-25T02:14:42Z)
• Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation [85.96410825961966]
  We argue that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries. To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions. We apply this proposed framework, called Point-Set Anchors, to object detection, instance segmentation, and human pose estimation.
  arXiv  Detail & Related papers  (2020-07-06T15:59:56Z)

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.