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IML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification
SINGH, Inder Pal; OYEDOTUN, Oyebade; GHORBEL, Enjie et al.
2022In AAAI-22 Workshop Program-Deep Learning on Graphs: Methods and Applications
Peer reviewed
 

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This work was funded by the Luxembourg National Research Fund (FNR), under the project reference BRIDGES2020/IS/14755859/MEET-A/Aouada
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Keywords :
Computer Vision; Graph Convolutional Network; Multi-label Image Classification; Machine Learning; Deep Learning; Artificial Intelligence
Abstract :
[en] In this paper, we propose the Improved Multi-Label Graph Convolutional Network (IML-GCN) as a precise and efficient framework for multi-label image classification. Although previous approaches have shown great performance, they usually make use of very large architectures. To handle this, we propose to combine the small version of a newly introduced network called TResNet with an extended version of Multi-label Graph Convolution Networks (ML-GCN); therefore ensuring the learning of label correlation while reducing the size of the overall network. The proposed approach considers a novel image feature embedding instead of using word embeddings. In fact, the latter are learned from words and not images making them inadequate for the task of multi-label image classification. Experimental results show that our framework competes with the state-of-the-art on two multi-label image benchmarks in terms of both precision and memory requirements.
Research center :
SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg
Disciplines :
Computer science
Author, co-author :
SINGH, Inder Pal  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
OYEDOTUN, Oyebade ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
GHORBEL, Enjie  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
AOUADA, Djamila  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
External co-authors :
no
Language :
English
Title :
IML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification
Publication date :
February 2022
Event name :
AAAI Conference on Artificial Intelligence Workshops
Event organizer :
Association for the Advancement of Artificial Intelligence
Event place :
Vancouver, Canada
Event date :
from 22-02-2022 to 01-03-2022
Audience :
International
Journal title :
AAAI-22 Workshop Program-Deep Learning on Graphs: Methods and Applications
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Funders :
FNR - Fonds National de la Recherche [LU]
Commentary :
This work was funded by the Luxembourg National Research Fund (FNR), under the project reference BRIDGES2020/IS/14755859/MEET-A/Aouada.
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since 10 January 2022

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