Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation

• URL: http://arxiv.org/abs/2007.02846v4
• Date: Mon, 3 Aug 2020 06:14:19 GMT
• Title: Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation
• Authors: Fangyun Wei, Xiao Sun, Hongyang Li, Jingdong Wang, Stephen Lin
• Abstract summary: 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.
• Score: 85.96410825961966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A recent approach for object detection and human pose estimation is to regress bounding boxes or human keypoints from a central point on the object or person. While this center-point regression is simple and efficient, we argue that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries, due to object deformation and scale/orientation variation. To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions. This point set is arranged to reflect a good initialization for the given task, such as modes in the training data for pose estimation, which lie closer to the ground truth than the central point and provide more informative features for regression. As the utility of a point set depends on how well its scale, aspect ratio and rotation matches the target, we adopt the anchor box technique of sampling these transformations to generate additional point-set candidates. We apply this proposed framework, called Point-Set Anchors, to object detection, instance segmentation, and human pose estimation. Our results show that this general-purpose approach can achieve performance competitive with state-of-the-art methods for each of these tasks. Code is available at \url{https://github.com/FangyunWei/PointSetAnchor}

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)
• LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves [8.037214110171123]
  Lane detection plays a critical role in the field of autonomous driving. We propose a novel approach, LanePtrNet, which treats lane detection as a process of point voting and grouping on ordered sets. We conduct comprehensive experiments to validate the effectiveness of our proposed approach, demonstrating its superior performance.
  arXiv  Detail & Related papers  (2024-03-08T08:45:42Z)
• 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)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings [17.04471874483516]
  Existing approaches either compute dense keypoint embeddings in a single forward pass, or allocate their full capacity to a sparse set of points. In this paper we explore a middle ground based on the observation that the number of relevant points at a given time are typically relatively few. Our main contribution is a novel architecture, inspired by few-shot task adaptation, which allows a sparse-style network to condition on a keypoint embedding.
  arXiv  Detail & Related papers  (2021-12-09T13:25:42Z)
• Oriented RepPoints for Aerial Object Detection [10.818838437018682]
  In this paper, we propose a novel approach to aerial object detection, named Oriented RepPoints. Specifically, we suggest to employ a set of adaptive points to capture the geometric and spatial information of the arbitrary-oriented objects. To facilitate the supervised learning, the oriented conversion function is proposed to explicitly map the adaptive point set into an oriented bounding box.
  arXiv  Detail & Related papers  (2021-05-24T06:18:23Z)
• Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression [81.05772887221333]
  We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. We present a simple yet effective approach, named disentangled keypoint regression (DEKR) We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods.
  arXiv  Detail & Related papers  (2021-04-06T05:54:46Z)
• 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)
• Robust 6D Object Pose Estimation by Learning RGB-D Features [59.580366107770764]
  We propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-02-29T06:24:55Z)

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.