NDLI: Semantic combined network for zero-shot scene parsing

Content Provider	IET Digital Library
Author	Wang, Yinduo Zhang, Haofeng Wang, Shidong Long, Yang Yang, Longzhi
Abstract	Recently, image-based scene parsing has attracted increasing attention due to its wide application. However, conventional models can only be valid on images with the same domain of the training set and are typically trained using discrete and meaningless labels. Inspired by the traditional zero-shot learning methods which employ auxiliary side information to bridge the source and target domains, the authors propose a novel framework called semantic combined network (SCN), which aims at learning a scene parsing model only from the images of the seen classes while targeting on the unseen ones. In addition, with the assistance of semantic embeddings of classes, the proposed SCN can further improve the performances of traditional fully supervised scene parsing methods. Extensive experiments are conducted on the data set Cityscapes, and the results show that the proposed SCN can perform well on both zero-shot scene parsing (ZSSP) and generalised ZSSP settings based on several state-of-the-art scenes parsing architectures. Furthermore, the authors test the proposed model under the traditional fully supervised setting and the results show that the proposed SCN can also significantly improve the performances of the original network models.
Starting Page	757
Ending Page	765
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 4, Mar (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0870
Journal	IET Image Processing
Publisher Date	2019-11-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Discrete Labels Generalised ZSSP Settings Image-based Scene Parsing Knowledge Engineering Technique Learning in AI Meaningless Labels Natural Language Processing Object Detection Object Recognition Optical, Image And Video Signal Processing Original Network Model Scene Parsing Model SCN Semantic Combined Network Semantic Embeddings State-of-the-art Scenes Target Domain Traditional Fully Supervised Scene Parsing Method Traditional Fully Supervised Setting Training Set Unsupervised Learning Zero-shot Scene Parsing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Weakly supervised classification model for zero-shot semantic segmentation

Unbiased hybrid generation network for zero-shot learning

Jointly network image processing: multi-task image semantic segmentation of indoor scene based on CNN

Horizon detection in maritime images using scene parsing network

Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds

Zero-Shot Object Recognition Using Semantic Label Vectors

Visual tracking based on semantic and similarity learning

PFCN: a fully convolutional network for point cloud semantic segmentation

Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning

Semantic combined network for zero-shot scene parsing

Similar Documents

Weakly supervised classification model for zero-shot semantic segmentation

Unbiased hybrid generation network for zero-shot learning

Jointly network image processing: multi-task image semantic segmentation of indoor scene based on CNN

Horizon detection in maritime images using scene parsing network

Learning Adversarial Semantic Embeddings for Zero-Shot Recognition in Open Worlds

Zero-Shot Object Recognition Using Semantic Label Vectors

Visual tracking based on semantic and similarity learning

PFCN: a fully convolutional network for point cloud semantic segmentation

Webly Supervised Semantic Embeddings for Large Scale Zero-Shot Learning

Semantic combined network for zero-shot scene parsing