Reference : MULTI LABEL IMAGE CLASSIFICATION USING ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS (ML-AGCN)
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/51776
MULTI LABEL IMAGE CLASSIFICATION USING ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS (ML-AGCN)
English
Singh, Inder Pal mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Ghorbel, Enjie mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Oyedotun, Oyebade mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Aouada, Djamila mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
2022
IEEE International Conference on Image Processing
Yes
International
2022 IEEE International Conference on Image Processing (ICIP)
15-10-2022 to 19-10-2022
IEEE
Bordeaux
France
[en] Computer Vision ; Machine Learning ; Multi-label Image Classification ; Graph Convolutional Networks ; Deep Learning ; Image Processing
[en] In this paper, a novel graph-based approach for multi-label image classification called Multi-Label Adaptive Graph Convolutional Network (ML-AGCN) is introduced. Graph-based methods have shown great potential in the field of multi-label classification. However, these approaches heuristically fix the graph topology for modeling label dependencies, which might be not optimal. To handle that, we propose to learn the topology in an end-to-end manner. Specifically, we incorporate an attention-based mechanism for estimating the pairwise importance between graph nodes and a similarity-based mechanism for conserving the feature similarity between different nodes. This offers a more flexible way for adaptively modeling the graph. Experimental results are reported on two well-known datasets, namely, MS-COCO and VG-500. Results show that ML-AGCN outperforms state-of-the-art methods while reducing the number of model parameters.
SnT - Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg
Fonds National de la Recherche - FnR
MEET-A
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/51776
FnR ; FNR14755859 > Djamila Aouada > MEET-A > Multi-modal Fusion Of Electro-optical Sensors For Spacecraft Pose Estimation Towards Autonomous In-orbit Operations > 01/01/2021 > 31/12/2023 > 2020

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