Nondiscriminatory Treatment: a straightforward framework for multi-human parsing

• URL: http://arxiv.org/abs/2101.10913v1
• Date: Tue, 26 Jan 2021 16:31:21 GMT
• Title: Nondiscriminatory Treatment: a straightforward framework for multi-human parsing
• Authors: Min Yan, Guoshan Zhang, Tong Zhang, Yueming Zhang
• Abstract summary: Multi-human parsing aims to segment every body part of every human instance. We present an end-to-end and box-free pipeline from a new and more human-intuitive perspective. Experiments show that our network performs superiorly against state-of-the-art methods.
• Score: 14.254424142949741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-human parsing aims to segment every body part of every human instance. Nearly all state-of-the-art methods follow the "detection first" or "segmentation first" pipelines. Different from them, we present an end-to-end and box-free pipeline from a new and more human-intuitive perspective. In training time, we directly do instance segmentation on humans and parts. More specifically, we introduce a notion of "indiscriminate objects with categorie" which treats humans and parts without distinction and regards them both as instances with categories. In the mask prediction, each binary mask is obtained by a combination of prototypes shared among all human and part categories. In inference time, we design a brand-new grouping post-processing method that relates each part instance with one single human instance and groups them together to obtain the final human-level parsing result. We name our method as Nondiscriminatory Treatment between Humans and Parts for Human Parsing (NTHP). Experiments show that our network performs superiorly against state-of-the-art methods by a large margin on the MHP v2.0 and PASCAL-Person-Part datasets.

Related papers

• Single-stage Multi-human Parsing via Point Sets and Center-based Offsets [28.70266615856546]
  We present a high-performance Single-stage Multi-human Parsing architecture that decouples the multi-human parsing problem into two fine-grained sub-problems. The proposed method requires fewer training epochs and a less complex model architecture. In particular, the proposed method requires fewer training epochs and a less complex model architecture.
  arXiv  Detail & Related papers  (2023-04-22T09:30:50Z)
• RepParser: End-to-End Multiple Human Parsing with Representative Parts [74.31841289680563]
  We present an end-to-end multiple human parsing framework using representative parts, termed Rep. Rep solves the multiple human parsing in a new single-stage manner without resorting to person detection or post-grouping.
  arXiv  Detail & Related papers  (2022-08-27T02:22:24Z)
• AIParsing: Anchor-free Instance-level Human Parsing [98.80740676794254]
  We have designed an instance-level human parsing network which is anchor-free and solvable on a pixel level. It consists of two simple sub-networks: an anchor-free detection head for bounding box predictions and an edge-guided parsing head for human segmentation. Our method achieves the best global-level and instance-level performance over state-of-the-art one-stage top-down alternatives.
  arXiv  Detail & Related papers  (2022-07-14T12:19:32Z)
• Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity [59.1823948436411]
  We propose a novel approach for mask proposals, Generic Grouping Networks (GGNs) Our approach combines a local measure of pixel affinity with instance-level mask supervision, producing a training regimen designed to make the model as generic as the data diversity allows.
  arXiv  Detail & Related papers  (2022-04-12T22:37:49Z)
• Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing [65.87931036949458]
  Part-level Action Parsing aims at part state parsing for boosting action recognition in videos. We present a simple yet effective approach, named disentangled action parsing (DAP)
  arXiv  Detail & Related papers  (2021-11-05T02:29:32Z)
• End-to-end One-shot Human Parsing [91.5113227694443]
  One-shot human parsing (OSHP) task requires parsing humans into an open set of classes defined by any test example. End-to-end One-shot human Parsing Network (EOP-Net) proposed. EOP-Net outperforms representative one-shot segmentation models by large margins.
  arXiv  Detail & Related papers  (2021-05-04T01:35:50Z)
• 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)
• Identity-Guided Human Semantic Parsing for Person Re-Identification [42.705908907250986]
  We propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID.
  arXiv  Detail & Related papers  (2020-07-27T12:12:27Z)

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.