atlas

Research map / Label-efficient and robust learning / Recognition and long-tail

CDGNet: Class Distribution Guided Network for Human Parsing

CVPR 2022 · Label-efficient and robust learning › Recognition and long-tail · approach: Loss design and classifier theory · 47 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add horizontal and vertical class-distribution guidance to CE2P (ablation)CE2P · LIP validationmIoU: 53.10 -> 57.72Helped
Add IoU loss to CE2P with CDG (ablation)CE2P + CDG · LIP validationmIoU: 57.72 -> 59.04Helped
Add a non-local module to CE2P with CDG and IoU loss (ablation)CE2P + CDG + IoU loss · LIP validationmIoU: 59.04% to 59.93%Helped
Use CDGNet instead of CE2PCE2P · LIP validationmIoU: 53.10 -> 60.30Helped
Use CDGNet instead of the strongest listed baselineHuman parsing models · CIHPmIoU: 65.56; 61.05Helped
Use CDGNet instead of CorrPMHuman parsing models · ATR testPre.: 87.46; 89.18Hurt
Use CDGNet instead of HHPHuman parsing models · ATR testRecall: 86.87; 88.35Hurt

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy