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|a 10.1109/TIP.2024.3485498
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|a eng
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1 |
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|a Diao, Haiwen
|e verfasserin
|4 aut
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|a GSSF
|b Generalized Structural Sparse Function for Deep Cross-Modal Metric Learning
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|c 2024
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|a Date Revised 08.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Cross-modal metric learning is a prominent research topic that bridges the semantic heterogeneity between vision and language. Existing methods frequently utilize simple cosine or complex distance metrics to transform the pairwise features into a similarity score, which suffers from an inadequate or inefficient capability for distance measurements. Consequently, we propose a Generalized Structural Sparse Function to dynamically capture thorough and powerful relationships across modalities for pair-wise similarity learning while remaining concise but efficient. Specifically, the distance metric delicately encapsulates two formats of diagonal and block-diagonal terms, automatically distinguishing and highlighting the cross-channel relevancy and dependency inside a structured and organized topology. Hence, it thereby empowers itself to adapt to the optimal matching patterns between the paired features and reaches a sweet spot between model complexity and capability. Extensive experiments on cross-modal and two extra uni-modal retrieval tasks (image-text retrieval, person re-identification, fine-grained image retrieval) have validated its superiority and flexibility over various popular retrieval frameworks. More importantly, we further discover that it can be seamlessly incorporated into multiple application scenarios, and demonstrates promising prospects from Attention Mechanism to Knowledge Distillation in a plug-and-play manner
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|a Journal Article
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|a Zhang, Ying
|e verfasserin
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| 700 |
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|a Gao, Shang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Jiawen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Long
|e verfasserin
|4 aut
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| 700 |
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|a Lu, Huchuan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 01., Seite 6241-6252
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|g volume:33
|g year:2024
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|u http://dx.doi.org/10.1109/TIP.2024.3485498
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