Weakly-supervised Learning
216 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find Weakly-supervised Learning models and implementationsMost implemented papers
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
gvalvano/multiscale-adversarial-attention-gates
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We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.
Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation
dmcv-ecnu/MindSpore_ModelZoo
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ICCV 2021
In this way, the graph topology of the whole point cloud can be effectively established by the introduced auxiliary supervision, such that the information propagation between the labeled and unlabeled points will be realized.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
rmcong/Hybrid-Label-SOD_TCSVT2022
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In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
durandtibo/wildcat.pytorch
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CVPR 2017
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
Learning from Video and Text via Large-Scale Discriminative Clustering
Discriminative clustering has been successfully applied to a number of weakly-supervised learning tasks.
Classification from Pairwise Similarity and Unlabeled Data
Supervised learning needs a huge amount of labeled data, which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high.
Tell Me Where to Look: Guided Attention Inference Network
AustinDoolittle/Pytorch-Gain
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CVPR 2018
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients.
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation.
Cross-task weakly supervised learning from instructional videos
DmZhukov/CrossTask
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CVPR 2019
In this paper we investigate learning visual models for the steps of ordinary tasks using weak supervision via instructional narrations and an ordered list of steps instead of strong supervision via temporal annotations.
