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Discovering Latent Classes for Semi-Supervised Semantic Segmentation
[article]
2020
arXiv
pre-print
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This paper studies the problem of semi-supervised semantic segmentation. ...
On unlabeled images, we predict a probability map for latent classes and use it as a supervision signal to learn semantic segmentation. ...
In this work, we propose an approach for semi-supervised semantic segmentation that does not discard any information. ...
arXiv:1912.12936v3
fatcat:caa2ip7qtnahpj5iwfubl5fdnu
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.12518v1 fatcat:x7atl553ejcgjciypo5o4ucfzu
We introduce a method that allows to automatically segment images into semantically meaningful regions without human supervision. ...
In cases where semantic regions might be hard for human to define and consistently label, our method is still able to find meaningful and consistent semantic classes. ...
Example of semantic classes discovered by clustering features from different layers for OpenEDS eye segmentation dataset. ...
arXiv:2107.12518v2
fatcat:xkj4pjigrnegxeyv34xrj4oaeq
PiCoCo: Pixelwise Contrast and Consistency Learning for Semi-Supervised Building Footprint Segmentation
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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semi-supervised semantic segmentation methods. ...
Semi-Supervised Semantic Segmentation Semi-supervised semantic segmentation is aimed at learning the segmentation models based on both labeled and unlabeled images [43] - [45] . ...
doi:10.1109/jstars.2021.3119286
fatcat:7xfdzrevnjeehbqpw5z2k45xq4
Mining Latent Classes for Few-shot Segmentation
[article]
2021
arXiv
pre-print
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Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. ...
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. ...
Motivated by this, we boost the few-shot segmentation via mining latent objects from the backgrounds. Semi-/self-supervised Learning. ...
arXiv:2103.15402v3
fatcat:zksnaw3pqnaj5boieyqpdf6vsm
Latent semantic modeling for slot filling in conversational understanding
2013
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
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Our method decomposes the task into two steps: latent n-gram clustering using a semi-supervised latent Dirichlet allocation (LDA) and sequence tagging for learning semantic structures in a CU system. ...
In this paper, we propose a new framework for semantic template filling in a conversational understanding (CU) system. ...
Experimental results for exploiting semi-supervised latent semantic information. ...
doi:10.1109/icassp.2013.6639285
dblp:conf/icassp/TurCH13
fatcat:qqqqwo7rrvdbfmlka7agooovme
Semi-automatic audio semantic concept discovery for multimedia retrieval
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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To address these issues, we propose a semi-automatic framework to discover the semantic concepts. We limit ourselves in audio modality here. ...
Previous work explored semantic concepts for content analysis to assist retrieval. ...
CONCLUSION In this paper, we present a novel framework to discover audio semantic concepts semi-automatically. ...
doi:10.1109/icassp.2014.6853822
dblp:conf/icassp/WangRM14a
fatcat:h5od6ae3njadbbut3czjlavwgi
Unsupervised Domain Adaptation in Semantic Segmentation: a Review
[article]
2020
arXiv
pre-print
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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. ...
This task is attracting a wide interest, since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment ...
We start this section by presenting some weakly-and semi-supervised learning methods for semantic segmentation. ...
arXiv:2005.10876v1
fatcat:7t5v6qibxnfcxhwtohqqunhd2u
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
[article]
2015
arXiv
pre-print
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We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations. ...
It facilitates to reduce search space for segmentation effectively by exploiting class-specific activation maps obtained from bridging layers. ...
Conclusion We proposed a novel deep neural network architecture for semi-supervised semantic segmentation with heterogeneous annotations, where classification and segmentation networks are decoupled for ...
arXiv:1506.04924v2
fatcat:kdljwoets5h53jes3iwph75ypy
Unsupervised Domain Adaptation in Semantic Segmentation: A Review
2020
Technologies
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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. ...
This task is attracting a wide interest since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment ...
We start this section by presenting some weakly-and semi-supervised learning methods for semantic segmentation. ...
doi:10.3390/technologies8020035
fatcat:qzgjjiw5p5bldk76mh3s3pwlfq
Learning Saliency Propagation for Semi-Supervised Instance Segmentation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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The results show our method establishes new states of the art for semi-supervised instance segmentation. 1 ...
Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. ...
Note that this is different from previous works that utilize MIL to discover class-specific responses in the image for semantic segmentation [29] . ...
doi:10.1109/cvpr42600.2020.01032
dblp:conf/cvpr/ZhouWJDY20
fatcat:qcak6ujxt5dolca4dtudmuezkm
Attribute Learning for Understanding Unstructured Social Activity
[chapter]
2012
Lecture Notes in Computer Science
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To solve this problem, we (1) contribute an unstructured social activity attribute (USAA) dataset with both visual and audio attributes, (2) introduce the concept of semi-latent attribute space and (3) ...
Recently, attribute learning has emerged as a promising paradigm for transferring learning to sparsely labelled classes in object or single-object short action classification. ...
Discovering and learning those discriminative yet latent attributes thus becomes the key. ...
doi:10.1007/978-3-642-33765-9_38
fatcat:nsltcn7qyjcwfmlct67ce6h5du
Factorised spatial representation learning: application in semi-supervised myocardial segmentation
[article]
2018
arXiv
pre-print
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We demonstrate the proposed method's utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. ...
In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. ...
We also thank NVIDIA Corporation for donating a Titan X GPU. ...
arXiv:1803.07031v2
fatcat:emcaprxdxrejfgo525kgqyjz2m
Learning Multimodal Latent Attributes
2014
IEEE Transactions on Pattern Analysis and Machine Intelligence
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model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. ...
To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic ...
Semi-latent semantic attribute space A p dimensional metric space where p ud dimensions encode manually specified semantic properties, and p la dimensions encode latent semantic properties determined by ...
doi:10.1109/tpami.2013.128
pmid:24356351
fatcat:tlchipuvl5evflsw6ewqs2oqyu
A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
[article]
2022
arXiv
pre-print
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and noisy supervision) and supplemented by the types of segmentation problems (including semantic segmentation, instance segmentation and panoptic segmentation). ...
Finally, we share our opinions about the future research directions for label-efficient deep segmentation. ...
Semi-supervised Segmentation
Semi-supervised semantic segmentation In this section, we review the methods for semi-supervised semantic segmentation, where only a small fraction of training images is ...
arXiv:2207.01223v1
fatcat:i7rgpxrfkrdbfm4effjdcjjr24
Adversarial Learning for Semi-Supervised Semantic Segmentation
[article]
2018
arXiv
pre-print
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We propose a method for semi-supervised semantic segmentation using an adversarial network. ...
In addition, the fully convolutional discriminator enables semi-supervised learning through discovering the trustworthy regions in predicted results of unlabeled images, thereby providing additional supervisory ...
Figure 1 : Overview of the proposed system for semi-supervised semantic segmentation. ...
arXiv:1802.07934v2
fatcat:vzcurbdrgzdfflikr6sdjup6um
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