Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains

• URL: http://arxiv.org/abs/2301.04494v5
• Date: Mon, 22 Jul 2024 08:16:26 GMT
• Title: Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains
• Authors: Indel Pal Singh, Enjie Ghorbel, Oyebade Oyedotun, Djamila Aouada,
• Abstract summary: This paper proposes an adaptive graph-based approach for multi-label image classification. It is done by integrating an attention-based mechanism and a similarity-preserving strategy. The proposed framework is then extended to multiple domains using an adversarial training scheme.
• Score: 8.02139126500224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes an adaptive graph-based approach for multi-label image classification. Graph-based methods have been largely exploited in the field of multi-label classification, given their ability to model label correlations. Specifically, their effectiveness has been proven not only when considering a single domain but also when taking into account multiple domains. However, the topology of the used graph is not optimal as it is pre-defined heuristically. In addition, consecutive Graph Convolutional Network (GCN) aggregations tend to destroy the feature similarity. To overcome these issues, an architecture for learning the graph connectivity in an end-to-end fashion is introduced. This is done by integrating an attention-based mechanism and a similarity-preserving strategy. The proposed framework is then extended to multiple domains using an adversarial training scheme. Numerous experiments are reported on well-known single-domain and multi-domain benchmarks. The results demonstrate that our approach achieves competitive results in terms of mean Average Precision (mAP) and model size as compared to the state-of-the-art. The code will be made publicly available.

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