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Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
[article]
2020
arXiv
pre-print
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CT and MRI, as well as cardiac segmentation for MRI. ...
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. ...
The authors would like to thank Konstantinos Kamnitsas and Zeju Li for insightful comments. ...
arXiv:2007.09886v2
fatcat:tnmhky4sn5cv5ojlvglmgvcti4
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2108.06600v1 fatcat:xpuw7e6jfrfz3b5iyat4en5k3a
Comprehensive ablation experiments and visualization studies also show the significant effect of SDPM and SAAM for few-shot segmentation task. ...
Few-shot semantic segmentation is a challenging task of predicting object categories in pixel-wise with only few annotated samples. However, existing approaches still face two main challenges. ...
ACKNOWLEDGMENT This work was supported by National Natural Science Foundation of China under Grants 62002005 and 62072021. ...
arXiv:2108.06600v2
fatcat:nmmjjmmc3bgv7bvc6rlmarb3am
A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data
2020
IEEE Transactions on Medical Imaging
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2021 pre-print arXiv:2110.09260v1 fatcat:rsscsuzwknhw3f3esnhz3q7o2m
In this work, we propose a unified framework for generalized low-shot (one- and few-shot) medical image segmentation based on distance metric learning (DML). ...
In our experiments on brain MRI and abdominal CT datasets, the proposed framework achieves superior performances for low-shot segmentation towards standard DNN-based (3D U-Net) and classical registration-based ...
Another advantage worth mentioning is that the MRE- Net naturally unifies both one-and few-shot learning with a single framework; in contrast, although extension to few-shot setting is possible for the ...
doi:10.1109/tmi.2020.3045775
pmid:33338014
fatcat:vjyusba7fver7i6sk3qfz7k5du
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning
[article]
2022
arXiv
pre-print
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To enable effective updating of multi-prototype classifier weights, we propose two constraints respectively for updating the prototypes w.r.t. all point features and for encouraging the learning of diverse ...
Addressing the annotation challenge in 3D Point Cloud segmentation has inspired research into weakly supervised learning. ...
Originated in few-shot learning, multi-prototype learning aims to address the challenging of fitting prototypical network [25] for multi-modal data distribution [5] , [26] , [27] by learning prototypes ...
arXiv:2205.03137v1
fatcat:bdu7ytk5pjaqnb62dzhtznnlom
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
[article]
2020
arXiv
pre-print
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The system includes a novel multi-task contextual network for food segmentation, a few-shot learning-based classifier built by limited training samples for food recognition, and an algorithm for 3D surface ...
This allows sequential food segmentation, recognition, and estimation of the consumed food volume, permitting fully automatic estimation of the nutrient intake for each meal. ...
ACKNOWLEDGMENT We would like to thank the Central Kitchen of the University Hospital "Inspelspital" and particularly Beat Blum, Thomas Walser, Vinzenz Meier for providing the menus, the recipes and the ...
arXiv:2003.08273v1
fatcat:xzyommppebg3bggg3373xiqgi4
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
[article]
2017
arXiv
pre-print
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Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. ...
We report state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime (for 5-shot 5-way, we are comparable to previous state-of-the-art) on the Omniglot dataset. ...
Acknowledgements We would like to thank Ben Poole, and Yihui Quek (both at Stanford University) for useful discussions, and brainstorming. ...
arXiv:1708.02735v1
fatcat:waumvdlzijggzpidmoma3guzly
Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Second, two categories of few-shot learning methods, i.e., the data-augmentation-based and the prior-knowledge-based, are introduced for the interpretation of remote sensing images. ...
This article gives a reference for scholars working on few-shot learning research in the remote sensing field. ...
Experimental Datasets This article collects and summarizes the existing published few-shot learning datasets for scene classification, semantic segmentation, and object detection. ...
doi:10.1109/jstars.2021.3052869
fatcat:ldos3sx6mvaapjkgsua73l7tve
A Comparative Review of Recent Few-Shot Object Detection Algorithms
[article]
2021
arXiv
pre-print
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Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data and the ...
This survey provides a comprehensive overview from current classic and latest achievements for few-shot object detection to future research expectations from manifold perspectives. ...
Apart from these surveys for generic few-shot learning, several surveys also put emphasis on specific applications of few-shot learning: COVID-19 diagnosis [66] , natural language processing [67] and ...
arXiv:2111.00201v1
fatcat:preckuym7zarndm4yjc7n4k2oi
Space-Time-Brightness Sampling Using an Adaptive Pixel-Wise Coded Exposure
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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A few recent studies have shown that with non-uniform space-time sampling, such as that implemented with pixel-wise coded exposure, one can go beyond this trade-off and achieve high efficiency for scene ...
We built a prototype camera that enables adaptive coding of patterns online to show the feasibility of the proposed adaptive coded exposure method. ...
In addition, we built a prototype camera and showed the feasibility of the real-time adaptive coding in real experiments. Our approach and current implementation have a few limitations. ...
doi:10.1109/cvprw.2018.00237
dblp:conf/cvpr/NagaharaSLG18
fatcat:wig2tlznyzh4tchjmlncvzd37u
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction
[article]
2021
arXiv
pre-print
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We present a pose adaptive few-shot learning procedure and a two-stage data interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for single-image 3D reconstruction. ...
We first perform an input mixup which, combined with a pose adaptive learning procedure, is helpful in learning 2D feature extraction and pose adaptive latent encoding. ...
.; and
construction by Learning a Hierarchy of Local and Global Kim, J. 2021. Adaptive Prototype Learning and Allocation
Shape Priors. ...
arXiv:2112.12484v1
fatcat:ht6sjw4o2zex5hhfi6k4w573ne
Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks
[article]
2022
arXiv
pre-print
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To push this field forward, we build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes ...
sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. ...
Leveraging prototypical networks from online continual few-shot learning [14, 42] , our work aims to build and analyze such a system for lifelong adaptive learning in HAR. ...
arXiv:2203.05692v1
fatcat:xgtaxzixsrdhhd7xcs7h4y7o7u
What's in a Gist? Towards an Unsupervised Gist Representation for Few-Shot Large Document Classification
[chapter]
2020
Lecture Notes in Computer Science
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To evaluate our purposed framework, we apply the gist to the task of semantic similarity, specifically to few-shot large document classification where documents on average have a large number of words. ...
In this paper, we investigate and introduce a novel unsupervised gist extraction and quantification framework that represents a computational form of the gist based on notions from fuzzy trace theory. ...
MAML: Model-Agnostic Meta-Learning is a few-shot method that meta-learns a prior over model parameters which allows the model to quickly adapt to unseen classes [8] where we set the number of inner steps ...
doi:10.1007/978-3-030-47426-3_21
fatcat:hi4syqmc4rcxznfnqq2qgxz33u
UniT: Unified Knowledge Transfer for Any-shot Object Detection and Segmentation
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.07502v2 fatcat:2rcxe7cxpjbudg4l3j4hdyxe3uOther Versions
Weakly-supervised approaches draw on image-level labels to build detectors/segmentors, while zero/few-shot methods assume abundant instance-level data for a set of base classes, and none to a few examples ...
Methods for object detection and segmentation rely on large scale instance-level annotations for training, which are difficult and time-consuming to collect. ...
Few-shot Object Detection and Segmentation Datasets. ...
arXiv:2006.07502v3
fatcat:5wtstqlbcfbdplg2qzbpmmyq4a
Wi-Fi-Based Location-Independent Human Activity Recognition with Attention Mechanism Enhanced Method
2022
Electronics
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Specifically, aiming to adapt the model well across locations with quite limited samples, we propose a Channel–Time–Subcarrier Attention Mechanism (CTS-AM) enhanced few-shot learning method that fulfills ...
Although great success has been achieved for location-dependent recognition tasks, it depends on adequate data collection, which is particularly laborious and time-consuming, being impractical for actual ...
Finally, an improved prototypical network for a few-shot, learning-based HAR method is presented and analyzed. ...
doi:10.3390/electronics11040642
fatcat:mbyqrjr7afdili4nlcpedykpo4
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
[article]
2022
arXiv
pre-print
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In this work we propose a HyperTransformer, a transformer-based model for few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. ...
Finally, we extend our approach to a semi-supervised regime utilizing unlabeled samples in the support set and further improving few-shot performance. ...
ACKNOWLEDGEMENTS We would like to thank Azalia Mirhoseini, David Ha, Bill Mark, Luke Metz, Raviteja Vemulapalli, Philip Mansfield, and Nolan Miller for insightful discussions. ...
arXiv:2201.04182v2
fatcat:vekerijxjffmngdn4oekoulfya
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