Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916330561601536 |
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| author | Singh, Indel Pal Ghorbel, Enjie Oyedotun, Oyebade Aouada, Djamila |
| author_facet | Singh, Indel Pal Ghorbel, Enjie Oyedotun, Oyebade Aouada, Djamila |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_04494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains Singh, Indel Pal Ghorbel, Enjie Oyedotun, Oyebade Aouada, Djamila Computer Vision and Pattern Recognition 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. |
| title | Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2301.04494 |