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Multi-domain semantic segmentation with overlapping labels
[article]
2021
arXiv
pre-print
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We address this challenge by proposing a principled method for seamless learning on datasets with overlapping classes based on partial labels and probabilistic loss. ...
Our method achieves competitive within-dataset and cross-dataset generalization, as well as ability to learn visual concepts which are not separately labeled in any of the training datasets. ...
Thus, we review semantic segmentation, efficient architectures, learning with partial labels, and multi-domain dense prediction. ...
arXiv:2108.11224v2
fatcat:qqxnvmcckbftbmonvaf4qx5cpq
A Fast Knowledge Distillation Framework for Visual Recognition
[article]
2021
arXiv
pre-print
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ReLabel, a recently proposed solution, suggests creating a label map for the entire image. ...
When conducting multi-crop in the same image for data loading, our FKD is even more efficient than the traditional image classification framework. ...
) for some outlier regions, our strategy is substantially more robust than ReLabel, such as the loose bounding boxes of objects, partial object, etc., as shown in Fig. 2
(row 3); (iii) In some particular ...
arXiv:2112.01528v1
fatcat:jppaeudnfreprkgaob3hiyxgha
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
[article]
2021
arXiv
pre-print
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This paper formulates this as a multi-source domain adaptation and label unification problem, and proposes a novel method for it. ...
In the latter, knowledge is transferred in the unified label space after a label completion process with pseudo-labels. ...
Standard supervised learning is then performed in the unified label space for the final model. ...
arXiv:2012.08385v2
fatcat:mov5x43bjjbgljzkcalkhyaxt4
Fairness-Aware Process Mining
[article]
2019
arXiv
pre-print
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for delays). ...
Process mining is a multi-purpose tool enabling organizations to improve their processes. ...
Note that in all the experiments the relabeling technique with no limitation on the label of the leaf have been used, which means, both leaves with + and -labels may get relabeled. ...
arXiv:1908.11451v1
fatcat:xxulxmoldrea5af47jonkcnpou
Deep Mining External Imperfect Data for Chest X-ray Disease Screening
[article]
2020
arXiv
pre-print
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Specifically, the imperfect data is in two folds: domain discrepancy, as the image appearances vary across datasets; and label discrepancy, as different datasets are partially labeled. ...
To this end, we formulate the multi-label thoracic disease classification problem as weighted independent binary tasks according to the categories. ...
[28] studied the multi-organ segmentation with partially-labeled domains and developed a prior-aware neural network. To address missing labels in multi-label classification, Yang et al. ...
arXiv:2006.03796v1
fatcat:4wbdav3cqvbo5d6dzy3ravnuia
Label-Noise Robust Multi-Domain Image-to-Image Translation
[article]
2019
arXiv
pre-print
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Multi-domain image-to-image translation is a problem where the goal is to learn mappings among multiple domains. ...
To overcome this limitation, we propose a novel model called the label-noise robust image-to-image translation model (RMIT) that can learn a clean label conditional generator even when noisy labeled data ...
Acknowledgement This work was partially supported by JST CREST Grant Number JPMJCR1403, Japan, and partially supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) as "Seminal ...
arXiv:1905.02185v1
fatcat:vbyukorjyrhanmfnm5usfilkxy
Monotonic classification: an overview on algorithms, performance measures and data sets
[article]
2018
arXiv
pre-print
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the target class label should not decrease when input attributes values increase). ...
There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfil restrictions of monotonicity (i.e. ...
The proposed method consists of two separated stages for learning and subsequent tuning. ...
arXiv:1811.07155v1
fatcat:h7uqontgl5gdtgr3bduu73wg44
Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
[article]
2021
arXiv
pre-print
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We also demonstrate extensions in a variety of settings: semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision ...
With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn their correct position in the image. ...
To improve the learning process and avoid overfitting, we make the adversarial game harder for the discriminator, using label noise [52] and instance noise [53] . ...
arXiv:2007.01152v3
fatcat:zoiczzbhmrgq5nqvs3omq3afva
Object Boundary Guided Semantic Segmentation
[article]
2016
arXiv
pre-print
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relabeled boundaries. ...
To tackle this problem, we introduce a double branch fully convolutional neural network, which separates the learning of the desirable semantic class labeling with mask-level object proposals guided by ...
The network learns to design tailored feature pools for a vision task by examining deep features of discriminative properties and shallow features of local visual patterns. ...
arXiv:1603.09742v4
fatcat:6vk75tdmjvgkpdqkkco35qejei
Discriminative, Semantic Segmentation of Brain Tissue in MR Images
[chapter]
2009
Lecture Notes in Computer Science
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It uses discriminative Random Decision Forest classification and takes into account partial volume effects. ...
This is combined with correction of intensities for the MR bias field, in conjunction with a learned model of spatial context, to achieve accurate voxel-wise classification. ...
We also showed that further improvements could be obtained in classification error performance by taking account of partial volume effect, and this suggests a modified test paradigm for future studies. ...
doi:10.1007/978-3-642-04271-3_68
fatcat:f7ufaksevvedldhtbzlajg2js4
Automatic Noisy Label Correction for Fine-Grained Entity Typing
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2205.03011v1 fatcat:rlyofmjtorgkbpadrzo4m5ghee
In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. ...
Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel ...
The authors would also like to thank the anonymous reviewers for their comments on improving the quality of this paper. ...
arXiv:2205.03011v2
fatcat:i64inxlgizbrpogomgepvgchie
Graph-shifts: Natural image labeling by dynamic hierarchical computing
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
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For low-level vision, we explore image restoration, and for high-level vision, we make use of a hybrid discriminative-generative model to segment and label images into semantically meaningful regions ( ...
In this paper, we present a new approach for image labeling based on the recently introduced graph-shifts algorithm. ...
Acknowledgements ZT and JJC (partially) are funded by NIH Grant U54 RR021813 entitled Center for Computational Biology. AY is funded by NSF 0413214. ...
doi:10.1109/cvpr.2008.4587490
dblp:conf/cvpr/CorsoYT08
fatcat:mbrbzit2evdmbh6tm3zgjnhwda
P-N learning: Bootstrapping binary classifiers by structural constraints
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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We propose a novel paradigm for training a binary classifier from labeled and unlabeled examples that we call P-N learning. ...
The learning process is guided by positive (P) and negative (N) constraints which restrict the labeling of the unlabeled set. ...
(iii)
Figure 3 . 3 P-N learning first trains a classifier from labeled data and then iterates over: (i) labeling of the unlabeled data by the classifier, (ii) identification and relabeling of examples ...
doi:10.1109/cvpr.2010.5540231
dblp:conf/cvpr/KalalMM10
fatcat:4tih4u5cx5ezbn3pp6ayorerb4
Integrated Foreground Segmentation and Boundary Matting for Live Videos
2015
IEEE Transactions on Image Processing
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The usage of two competing local classifiers, as we have advocated, provides higher discriminative power while allowing better handling of ambiguities. ...
By exploiting this proposed machine learning technique, and by addressing both foreground segmentation and boundary matting problems in an integrated manner, our algorithm is shown to be particularly competent ...
The partially trained 1SVMs are thus directly used to perform relabeling. ...
doi:10.1109/tip.2015.2401516
pmid:25675459
fatcat:hnnh6vvvwjeshdnfrkhc43pgyq
Data preprocessing techniques for classification without discrimination
2011
Knowledge and Information Systems
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discrimination without relabeling instances. ...
We first study the theoretically optimal trade-off between accuracy and non-discrimination for pure classifiers. ...
Acknowledgments We thank the anonymous reviewers for their insightful comments and the many suggestions that contributed substantially to the improvement of the document. ...
doi:10.1007/s10115-011-0463-8
fatcat:4xytwosddjhnhmri5nlovcmcse
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