Weakly supervised Semantic Segmentation

137 papers with code • 3 benchmarks • 4 datasets

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Most implemented papers

Puzzle-CAM: Improved localization via matching partial and full features

OFRIN/PuzzleCAM 27 Jan 2021

Weakly-supervised semantic segmentation (WSSS) is introduced to narrow the gap for semantic segmentation performance from pixel-level supervision to image-level supervision.

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

dmcv-ecnu/MindSpore_ModelZoo AAAI 2021

Firstly, we construct a pretext task, \textit{i. e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network.

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation

jiwoon-ahn/psa CVPR 2018

To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels.

Integral Object Mining via Online Attention Accumulation

PengtaoJiang/OAA-PyTorch ICCV 2019

In order to accumulate the discovered different object parts, we propose an online attention accumulation (OAA) strategy which maintains a cumulative attention map for each target category in each training image so that the integral object regions can be gradually promoted as the training goes.

Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

YudeWang/SEAM CVPR 2020

Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.

Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

GuoleiSun/MCIS_wsss ECCV 2020

Moreover, our approach ranked 1st place in the Weakly-Supervised Semantic Segmentation Track of CVPR2020 Learning from Imperfect Data Challenge.

Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs

biomedsciai/histocartography 4 Mar 2021

Thus, weakly-supervised semantic segmentation techniques are proposed to utilize weak supervision that is cheaper and quicker to acquire.

Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation

cvlab-yonsei/BANA CVPR 2021

We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations.

SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds

QingyongHu/SQN 11 Apr 2021

Labelling point clouds fully is highly time-consuming and costly.

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

eli-yili/pmm ICCV 2021

For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers.