Weakly supervised Semantic Segmentation
157 papers with code • 3 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Weakly supervised Semantic Segmentation
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nuScenes
SemanticKITTI
SemanticPOSS
CheXlocalize
Most implemented papers
Puzzle-CAM: Improved localization via matching partial and full features
OFRIN/PuzzleCAM
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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
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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 without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels
lizhuohong/paraformer
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CVPR 2024
However, it is still a non-trivial task hindered by complex ground details, various landforms, and the scarcity of accurate training labels over a wide-span geographic area.
Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation
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
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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
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
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
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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
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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
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Labelling point clouds fully is highly time-consuming and costly.
