DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929577570336768 |
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| author | Gebrerufael, Hadush Hailu Tiwari, Anil Kumar Neupane, Gaurav Hailu, Goitom Ybrah |
| author_facet | Gebrerufael, Hadush Hailu Tiwari, Anil Kumar Neupane, Gaurav Hailu, Goitom Ybrah |
| contents | Multi-modal deep metric learning is crucial for effectively capturing diverse representations in tasks such as face verification, fine-grained object recognition, and product search. Traditional approaches to metric learning, whether based on distance or margin metrics, primarily emphasize class separation, often overlooking the intra-class distribution essential for multi-modal feature learning. In this context, we propose a novel loss function called Density-Aware Adaptive Margin Loss(DAAL), which preserves the density distribution of embeddings while encouraging the formation of adaptive sub-clusters within each class. By employing an adaptive line strategy, DAAL not only enhances intra-class variance but also ensures robust inter-class separation, facilitating effective multi-modal representation. Comprehensive experiments on benchmark fine-grained datasets demonstrate the superior performance of DAAL, underscoring its potential in advancing retrieval applications and multi-modal deep metric learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05438 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning Gebrerufael, Hadush Hailu Tiwari, Anil Kumar Neupane, Gaurav Hailu, Goitom Ybrah Computer Vision and Pattern Recognition Machine Learning Multi-modal deep metric learning is crucial for effectively capturing diverse representations in tasks such as face verification, fine-grained object recognition, and product search. Traditional approaches to metric learning, whether based on distance or margin metrics, primarily emphasize class separation, often overlooking the intra-class distribution essential for multi-modal feature learning. In this context, we propose a novel loss function called Density-Aware Adaptive Margin Loss(DAAL), which preserves the density distribution of embeddings while encouraging the formation of adaptive sub-clusters within each class. By employing an adaptive line strategy, DAAL not only enhances intra-class variance but also ensures robust inter-class separation, facilitating effective multi-modal representation. Comprehensive experiments on benchmark fine-grained datasets demonstrate the superior performance of DAAL, underscoring its potential in advancing retrieval applications and multi-modal deep metric learning. |
| title | DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.05438 |