PBADet: A One-Stage Anchor-Free Approach for Part-Body Association

• URL: http://arxiv.org/abs/2402.07814v1
• Date: Mon, 12 Feb 2024 17:18:51 GMT
• Title: PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
• Authors: Zhongpai Gao, Huayi Zhou, Abhishek Sharma, Meng Zheng, Benjamin Planche, Terrence Chen, Ziyan Wu
• Abstract summary: PBADet is a one-stage, anchor-free approach for part-body association detection. Our design is inherently versatile and capable of managing multiple parts-to-body associations.
• Score: 30.6652836585336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The detection of human parts (e.g., hands, face) and their correct association with individuals is an essential task, e.g., for ubiquitous human-machine interfaces and action recognition. Traditional methods often employ multi-stage processes, rely on cumbersome anchor-based systems, or do not scale well to larger part sets. This paper presents PBADet, a novel one-stage, anchor-free approach for part-body association detection. Building upon the anchor-free object representation across multi-scale feature maps, we introduce a singular part-to-body center offset that effectively encapsulates the relationship between parts and their parent bodies. Our design is inherently versatile and capable of managing multiple parts-to-body associations without compromising on detection accuracy or robustness. Comprehensive experiments on various datasets underscore the efficacy of our approach, which not only outperforms existing state-of-the-art techniques but also offers a more streamlined and efficient solution to the part-body association challenge.

Related papers

• Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning [8.015703163954639]
  Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects. Most existing methods utilize the off-the-shelf pose or parsing networks as pseudo labels, which are prone to error. We propose a novel Occlusion Correction Network (OCNet) that corrects features through relational-weight learning and obtains diverse and representative features without using external networks.
  arXiv  Detail & Related papers  (2022-12-09T07:48:47Z)
• Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding [24.96237908232171]
  We propose a single-stage end-to-end trainable framework for tackling the HBOE problem with multi-persons. By integrating the prediction of bounding boxes and direction angles in one embedding, our method can jointly estimate the location and orientation of all bodies in one image.
  arXiv  Detail & Related papers  (2022-10-27T16:22:50Z)
• Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation [63.910211095033596]
  Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples. We propose a simple yet versatile framework in the spirit of divide-and-conquer. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information.
  arXiv  Detail & Related papers  (2022-04-21T06:21:14Z)
• Motion-Aware Transformer For Occluded Person Re-identification [1.9899263094148867]
  We propose a self-supervised deep learning method to improve the location performance for human parts through occluded person Re-ID. Unlike previous works, we find that motion information derived from the photos of various human postures can help identify major human body components.
  arXiv  Detail & Related papers  (2022-02-09T02:53:10Z)
• Mining the Benefits of Two-stage and One-stage HOI Detection [26.919979955155664]
  Two-stage methods have dominated Human-Object Interaction (HOI) detection for several years. One-stage methods are challenging to make an appropriate trade-off on multi-task learning, i.e., object detection, and interaction classification. We propose a novel one-stage framework with disentangling human-object detection and interaction classification in a cascade manner.
  arXiv  Detail & Related papers  (2021-08-11T07:38:09Z)
• InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation [37.80984212500406]
  We present a simple yet effective solution by employing instance-aware dynamic networks. Specifically, we propose an instance-aware module to adaptively adjust (part of) the network parameters for each instance. Our solution can significantly increase the capacity and adaptive-ability of the network for recognizing various poses, while maintaining a compact end-to-end trainable pipeline.
  arXiv  Detail & Related papers  (2021-07-19T15:56:09Z)
• Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing [131.97475877877608]
  A new bottom-up regime is proposed to learn category-level human semantic segmentation and multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities. Experiments on three instance-aware human datasets show that our model outperforms other bottom-up alternatives with much more efficient inference.
  arXiv  Detail & Related papers  (2021-03-08T06:55:00Z)
• Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search [65.51181219410763]
  One-step methods have been developed to handle pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. We propose a decoupled and memory-reinforced network (DMRNet) to overcome these problems.
  arXiv  Detail & Related papers  (2021-02-22T06:19:45Z)
• Centralized Information Interaction for Salient Object Detection [68.8587064889475]
  The U-shape structure has shown its advantage in salient object detection for efficiently combining multi-scale features. This paper shows that by centralizing these connections, we can achieve the cross-scale information interaction among them. Our approach can cooperate with various existing U-shape-based salient object detection methods by substituting the connections between the bottom-up and top-down pathways.
  arXiv  Detail & Related papers  (2020-12-21T12:42:06Z)
• Diverse Knowledge Distillation for End-to-End Person Search [81.4926655119318]
  Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. We propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck.
  arXiv  Detail & Related papers  (2020-12-21T09:04:27Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.