NDLI: From image-level to pixel-level labeling with Convolutional Networks

Content Provider	IEEE Xplore Digital Library
Author	Pinheiro, P.O. Collobert, R.
Copyright Year	2015
Description	Author affiliation: Idiap Res. Inst., Martigny, Switzerland (Pinheiro, P.O.; Collobert, R.)
Abstract	We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and naturally fits the Multiple Instance Learning (MIL) framework: every training image is known to have (or not) at least one pixel corresponding to the image class label, and the segmentation task can be rewritten as inferring the pixels belonging to the class of the object (given one image, and its object class). We propose a Convolutional Neural Network-based model, which is constrained during training to put more weight on pixels which are important for classifying the image. We show that at test time, the model has learned to discriminate the right pixels well enough, such that it performs very well on an existing segmentation benchmark, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object segmentation task by a large margin. We also compare the performance of our model with state of the art fully-supervised segmentation approaches.
Starting Page	1713
Ending Page	1721
File Size	449337
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7298780
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Training Smoothing methods Data models Computational modeling Feature extraction Object segmentation
Content Type	Text
Resource Type	Article
Subject	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

Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation

LOCUS: learning object classes with unsupervised segmentation

Transformation-Invariant Convolutional Jungles

Convolutional feature masking for joint object and stuff segmentation

Segmentation-Aware Deformable Part Models

Associative hierarchical CRFs for object class image segmentation

Hypercolumns for object segmentation and fine-grained localization

Pyramidal Model for Image Semantic Segmentation

Modeling scenes with local descriptors and latent aspects

From image-level to pixel-level labeling with Convolutional Networks

Similar Documents

Early Hierarchical Contexts Learned by Convolutional Networks for Image Segmentation

LOCUS: learning object classes with unsupervised segmentation

Transformation-Invariant Convolutional Jungles

Convolutional feature masking for joint object and stuff segmentation

Segmentation-Aware Deformable Part Models

Associative hierarchical CRFs for object class image segmentation

Hypercolumns for object segmentation and fine-grained localization

Pyramidal Model for Image Semantic Segmentation

Modeling scenes with local descriptors and latent aspects

From image-level to pixel-level labeling with Convolutional Networks