mlsanityData to 7 Oct 2026

Quality-Aware Network for Human Parsing

arXiv 2103.05997 ↗First posted 10 Mar 2021cs.CVcited by 13

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Abstract (arXiv)

How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing. In order to solve this problem, this work proposes a statistical method based on the output probability map to calculate the pixel quality information, which is called pixel score. In addition, the Quality-Aware Module (QAM) is proposed to fuse the different quality information, the purpose of which is to estimate the quality of human parsing results. We combine QAM with a concise and effective network design to propose Quality-Aware Network (QANet) for human parsing. Benefiting from the superiority of QAM and QANet, we achieve the best performance on three multiple and one single human parsing benchmarks, including CIHP, MHP-v2, Pascal-Person-Part and LIP. Without increasing the training and inference time, QAM improves the APr criterion by more than 10 points in the multiple human parsing task. QAM can be extended to other tasks with good quality estimation, e.g. instance segmentation. Specifically, QAM improves Mask R-CNN by ~1% mAP on COCO and LVISv1.0 datasets. Based on the proposed QAM and QANet, our overall system wins 1st place in CVPR2019 COCO DensePose Challenge, and 1st place in Track 1 & 2 of CVPR2020 LIP Challenge. Code and models are available at https://github.com/soeaver/QANet.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 59 of its 95 references that are in the map. First posted on arXiv 10 Mar 2021.

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59 of 95 references.

References · 59

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38 more
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Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 3.

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  1. 2021Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labelingcited by 84
  2. 2024Controllable Generation with Text-to-Image Diffusion Models: A Surveycited by 82
  3. 2023Cross-Covariate Gait Recognition: A Benchmarkcited by 21
  4. 2023Deep Learning Technique for Human Parsing: A Survey and Outlookcited by 18
  5. 2024Exploring More from Multiple Gait Modalities for Human Identificationcited by 12
  6. 2023What Decreases Editing Capability? Domain-Specific Hybrid Refinement for Improved GAN Inversioncited by 7
  7. 2023Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generationcited by 7
  8. 2022LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Spacecited by 4
  9. 2021Quality-Aware Network for Face Parsingcited by 3
  10. 2023BPJDet: Extended Object Representation for Generic Body-Part Joint Detectioncited by 2
  11. 2024E4C: Enhance Editability for Text-Based Image Editing by Harnessing Efficient CLIP Guidancecited by 2
  12. 2022SGM-Net: Semantic Guided Matting Netcited by 1
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  1. 2023Deep Learning for Human Parsing: A Surveycited by 1