[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