Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation
Download
index.pdf
Date
2022-01-01
Author
Amac, Mustafa Sercan
Sencan, Ahmet
Baran, Orhun Buğra
Ikizler-Cinbis, Nazli
Cinbiş, Ramazan Gökberk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
0
views
0
downloads
Cite This
Just like other few-shot learning problems, few-shot segmentation aims to minimize the need for manual annotation, which is particularly costly in segmentation tasks. Even though the few-shot setting reduces this cost for novel test classes, there is still a need to annotate the training data. To alleviate this need, we propose a self-supervised training approach for learning few-shot segmentation models. We first use unsupervised saliency estimation to obtain pseudo-masks on images. We then train a simple prototype based model over different splits of pseudo masks and augmentations of images. Our extensive experiments show that the proposed approach achieves promising results, highlighting the potential of self-supervised training. To the best of our knowledge this is the first work that addresses unsupervised few-shot segmentation problem on natural images.
Subject Keywords
Few-shot
,
Grouping and Shape
,
Semi- and Un- supervised Learning Segmentation
,
Transfer
URI
https://hdl.handle.net/11511/97712
DOI
https://doi.org/10.1109/wacv51458.2022.00050
Conference Name
22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning
Ertekin Bolelli, Şeyda (2021-06-09)
Data annotation for training of supervised learning algorithms has been a very costly procedure. The aim of deep active learning methodologies is to acquire the highest performance in supervised deep learning models by annotating as few data points as possible. As the feature space of data grows, the application of linear models in active learning settings has become insufficient. Therefore, Deep Bayesian Active Learning methodology which represents model uncertainty has been widely studied. In this paper, ...
Semi-supervised generative guidance for zero-shot semantic segmentation
Önem, Abdullah Cem; Cinbiş, Ramazan Gökberk; Department of Computer Engineering (2022-1)
Collecting fully-annotated data to train deep networks for semantic image segmentation can be prohibitively costly due to difficulty of making pixel-by-pixel annotations. In this context, zero-shot learning based formulations relax the labelled data requirements by enabling the recognition of classes without training examples. Recent studies on zero-shot learning of semantic segmentation models, however, highlight the difficulty of the problem. This thesis proposes techniques towards improving zero-shot gen...
Time memory trade off attack on symmetric ciphers
Saran, A. Nurdan; Doğanaksoy, Ali; Department of Cryptography (2009)
Time Memory Trade O (TMTO) is a cryptanalytic method that aims to develop an attack which has a lower memory complexity than lookup table and a lower online time complexity than exhaustive search. TMTO methods are widely studied in the literature and used for inverting various cryptosystems. We focus on the design and the analysis of TMTO on symmetric ciphers in this thesis. Firstly, the summary of the random mapping statistics from the view point of TMTO is presented. We also recalculate some expected valu...
Segmentation Fusion for Building Detection Using Domain-Specific Information
Karadag, Ozge Oztimur; Senaras, Caglar; Yarman Vural, Fatoş Tunay (2015-07-01)
Segment-based classification is one of the popular approaches for object detection, where the performance of the classification task is sensitive to the accuracy of the output of the initial segmentation. Majority of the object detection systems directly use one of the generic segmentation algorithms, such as mean shift or k-means. However, depending on the problem domain, the properties of the regions such as size, color, texture, and shape, which are suitable for classification, may vary. Besides, fine tu...
Recursive shortest spanning tree algorithms for image segmentation
Bayramoglu, NY; Bazlamaçcı, Cüneyt Fehmi (2005-11-24)
Image segmentation has an important role in image processing and the speed of the segmentation algorithm may become a drawback for some applications. This study analyzes the run time performances of some variations of the Recursive Shortest Spanning Tree Algorithm (RSST) and proposes simple but effective modifications on these algorithms to improve their speeds. In addition, the effect of link weight cost function on the run time performance and the segmentation quality is examined. For further improvement ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. S. Amac, A. Sencan, O. B. Baran, N. Ikizler-Cinbis, and R. G. Cinbiş, “MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation,” presented at the 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, Hawaii, Amerika Birleşik Devletleri, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97712.