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Weakly Supervised Semantic Segmentation via Progressive Patch Learning
[article]
2022
arXiv
pre-print
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Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset e.g., with 69.6surpasses most existing weakly supervised semantic segmentation methods. ...
"Progressive Patch Learning" further extends the feature destruction and patch learning to multi-level granularities in a progressive manner. ...
To reduce the effort of human labeling, weakly-supervised semantic segmentation (WSSS) has been studied for years, and various types of weak annotations, e.g., image-level [4] , [5] , [6] , [7] , ...
arXiv:2209.07828v1
fatcat:bkre6loevzgefnoraoq2ipvngq
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
[article]
2021
arXiv
pre-print
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As a part of this paper, we introduced a new weakly-supervised semantic segmentation (WSSS) dataset for lung adenocarcinoma (LUAD-HistoSeg). ...
In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision. ...
Then the well-trained PR-net could be transferred to a gland segmentation network in a semi-supervised learning manner. 4) Weakly-supervised Learning: Besides scarcer annotations with semi-supervised learning ...
arXiv:2110.08048v1
fatcat:vw2fptt5cbeijptzctyrw7xx5a
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
[article]
2022
arXiv
pre-print
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To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques ...
Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. ...
In computer vision, some researchers are focusing on using image-level labels to achieve semantic segmentation, called weakly-supervised semantic segmentation (WSSS). ...
arXiv:2204.06455v2
fatcat:o75sroudm5dr3mxyjgxkxvo54q
Puzzle-CAM: Improved localization via matching partial and full features
[article]
2021
arXiv
pre-print
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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. ...
To address this issue, we propose Puzzle-CAM, a process that minimizes differences between the features from separate patches and the whole image. ...
Recently, fully-supervised semantic segmentation (FSSS) has achieved remarkable progress [1, 2, 3] . ...
arXiv:2101.11253v3
fatcat:nrlcgk4ddfdivid7cmfyuj7uma
Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
2020
Applied Sciences
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Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. ...
Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. ...
These weakly supervised semantics segmentation methods can be roughly divided into two categories: traditional methods and deep learning methods. ...
doi:10.3390/app10051679
fatcat:qkyr6n6nwrfinivhbdc2pyqn4u
Weakly Supervised Nuclei Segmentation via Instance Learning
[article]
2022
arXiv
pre-print
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In this paper, we propose to decouple weakly supervised semantic and instance segmentation in order to enable more effective subtask learning and to promote instance-aware representation learning. ...
Weakly supervised nuclei segmentation is a critical problem for pathological image analysis and greatly benefits the community due to the significant reduction of labeling cost. ...
CONCLUSION In this paper, we have developed a novel framework for weakly supervised nuclei segmentation via an instance-aware learning strategy. ...
arXiv:2202.01564v2
fatcat:2vnfd5szlraovowsuhvpzejnja
Special issue on "visual semantic analysis with weak supervision"
2017
Multimedia Systems
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This special issue will target the most recent technical progresses on learning techniques for visual semantic understanding with weak supervision, such as weakly labeled representative views. ...
To effectively fill the semantic gap of these visual media, weakly supervised learning paradigms are developed, focusing on an intelligent mechanism that transfers the image/video-level semantics to different ...
doi:10.1007/s00530-016-0527-4
fatcat:72hcjiiwfzbk7mdrsumuia7rzy
Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation
[article]
2023
arXiv
pre-print
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Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches ...
Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. ...
By such, we finally build a bridge from the prototype learning to semantic segmentation. [6] with ground-truth annotations, and several weakly supervised baselines including Grad-Cam [16] , Grad-Cam+ ...
arXiv:2211.14491v2
fatcat:nd3okd63wzevjm4ctx74bgpcju
Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping – Challenges and Opportunities
[article]
2020
arXiv
pre-print
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Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale ...
Usually, the basis of this task is formed by (supervised) machine learning models. ...
Ghamisi, for making the challenge of weakly supervised learning for global land cover mapping the topic of the 2020 IEEE-GRSS Data Fusion Contest; and for numerous fruitful discussions during the design ...
arXiv:2002.08254v2
fatcat:4vz6isa46jbsxdqrjwtrrbglfm
Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation
2019
IEEE Transactions on Image Processing
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2018 pre-print arXiv:1804.04882v1 fatcat:f4hrdxlt7bh37c422lhbysq55iOther Versions
With this paper we introduce a novel weakly-supervised semantic segmentation model able to learn from image labels, and just image labels. ...
semantic segmentation methods that use image tags only, and even some models that leverage additional supervision or training data. ...
Weakly-Supervised Learning by Switching Loss Functions We here introduce our weakly-supervised learning model. ...
doi:10.1109/tip.2019.2901393
pmid:30802862
fatcat:h6foa52j65h53a2c7m4sof56ci
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
[article]
2023
arXiv
pre-print
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Compared to conventional semantic segmentation with pixel-level supervision, Weakly Supervised Semantic Segmentation (WSSS) with image-level labels poses the challenge that it always focuses on the most ...
discriminative regions, resulting in a disparity between fully supervised conditions. ...
Related Work Weakly Supervised Semantic Segmentation (Ru et al. 2022) explores the intrinsic architecture of ViT and derives reliable semantic affinity from multi-head self-attention for pseudo label ...
arXiv:2312.08916v2
fatcat:d64ghmlp2nesto63daobfpjw4m
Weakly-Supervised Simultaneous Evidence Identification and Segmentation for Automated Glaucoma Diagnosis
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this paper, we propose an innovative Weakly-Supervised Multi-Task Learning method (WSMTL) for accurate evidence identification, optic disc segmentation and automated glaucoma diagnosis. ...
Evidence identification, optic disc segmentation and automated glaucoma diagnosis are the most clinically significant tasks for clinicians to assess fundus images. ...
Figure 1 : 1 Weakly-Supervised Multi-Task Learning. (a) Weakly-Supervised Multi-Task Learning Settings. ...
doi:10.1609/aaai.v33i01.3301809
fatcat:ew2gyjxrrve5bip7hm42dgoapu
One-Shot Informed Robotic Visual Search in the Wild
[article]
2020
arXiv
pre-print
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We propose and evaluate a weakly supervised video representation learning method that outperforms ImageNet embeddings for similarity tasks in the underwater domain. ...
In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar ...
We do this via the visual navigation method in [62] , based on our existing work. This method learns a vision-based Bayesian Neural Network policy via fully-supervised imitation learning. ...
arXiv:2003.10010v2
fatcat:sm6eabviwjfnrpwf4fscae3co4
WEAKLY SUPERVISED SEMANTIC SEGMENTATION OF SATELLITE IMAGES FOR LAND COVER MAPPING – CHALLENGES AND OPPORTUNITIES
2020
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
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Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale ...
Usually, the basis of this task is formed by (supervised) machine learning models. ...
Ghamisi, for making the challenge of weakly supervised learning for global land cover mapping the topic of the 2020 IEEE-GRSS Data Fusion Contest; and for numerous fruitful discussions during the design ...
doi:10.5194/isprs-annals-v-3-2020-795-2020
fatcat:zmaqvbtsobfpbd55llrka5lvem
WTS: A Weakly towards Strongly Supervised Learning Framework for Remote Sensing Land Cover Classification Using Segmentation Models
2021
Remote Sensing
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Weakly supervised learning methods from weakly labeled annotations can overcome this difficulty to some extent and achieve impressive segmentation results, but results are limited in accuracy. ...
Inspired by point supervision and the traditional segmentation method of seeded region growing (SRG) algorithm, a weakly towards strongly (WTS) supervised learning framework is proposed in this study for ...
Conclusions and Future Work In order to deal with the insufficiency of pixel-level annotations for training semantic segmentation models, a weakly towards strongly (WTS) supervised learning framework is ...
doi:10.3390/rs13030394
fatcat:dstcx5rujbbbrblunf6zgbbzmi
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