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250508s2025 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2025.3556531
|2 doi
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|a pubmed25n1396.xml
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|a (DE-627)NLM386681643
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Zhang, Shizhou
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Prompt-Based Modality Alignment for Effective Multi-Modal Object Re-Identification
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|c 2025
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 05.05.2025
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a A critical challenge for multi-modal Object Re-Identification (ReID) is the effective aggregation of complementary information to mitigate illumination issues. State-of-the-art methods typically employ complex and highly-coupled architectures, which unavoidably result in heavy computational costs. Moreover, the significant distribution gap among different image spectra hinders the joint representation of multi-modal features. In this paper, we propose a framework named as PromptMA to establish effective communication channels between different modality paths, thereby aggregating modal complementary information and bridging the distribution gap. Specifically, we inject a series of learnable multi-modal prompts into the Image Encoder and introduce a prompt exchange mechanism to enable the prompts to alternately interact with different modal token embeddings, thus capturing and distributing multi-modal features effectively. Building on top of the multi-modal prompts, we further propose Prompt-based Token Selection (PBTS) and Prompt-based Modality Fusion (PBMF) modules to achieve effective multi-modal feature fusion while minimizing background interference. Additionally, due to the flexibility of our prompt exchange mechanism, our method is well-suited to handle scenarios with missing modalities. Extensive evaluations are conducted on four widely used benchmark datasets and the experimental results demonstrate that our method achieves state-of-the-art performances, surpassing the current benchmarks by over 15% on the challenging MSVR310 dataset and by 6% on the RGBNT201. The code is available at https://github.com/FHR-L/PromptMA
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Luo, Wenlong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheng, De
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xing, Yinghui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liang, Guoqiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Peng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yanning
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 34(2025) vom: 05., Seite 2450-2462
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
1 |
8 |
|g volume:34
|g year:2025
|g day:05
|g pages:2450-2462
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2025.3556531
|3 Volltext
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|d 34
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