DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning

• URL: http://arxiv.org/abs/2410.05438v2
• Date: Tue, 5 Nov 2024 18:44:55 GMT
• Title: DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning
• Authors: Hadush Hailu Gebrerufael, Anil Kumar Tiwari, Gaurav Neupane, Goitom Ybrah Hailu,
• Abstract summary: We propose a novel loss function called Density-Aware Adaptive Margin Loss (DAAL) DAAL preserves the density distribution of embeddings while encouraging the formation of adaptive sub-clusters within each class. Experiments on benchmark fine-grained datasets demonstrate the superior performance of DAAL.
• Score: 1.9472493183927981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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