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SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking.

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Title: SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking.
Authors: Guo, Gaowei1 (AUTHOR) guogaowei22@nudt.edu.cn, Li, Zhaoxu1 (AUTHOR), An, Wei1 (AUTHOR), Wang, Yingqian1 (AUTHOR), He, Xu1 (AUTHOR), Luo, Yihang1 (AUTHOR), Ling, Qiang1 (AUTHOR) lingqiang16@nudt.edu.cn, Li, Miao1 (AUTHOR), Lin, Zaiping1 (AUTHOR)
Source: Remote Sensing. Aug2024, Vol. 16 Issue 16, p2975. 24p.
Subjects: Transformer models, Design templates, Generalization, Data modeling
Abstract: Compared to hyperspectral trackers that adopt the "pre-training then fine-tuning" training paradigm, those using the "pre-training then prompt-tuning" training paradigm can inherit the expressive capabilities of the pre-trained model with fewer training parameters. Existing hyperspectral trackers utilizing prompt learning lack an adequate prompt template design, thus failing to bridge the domain gap between hyperspectral data and pre-trained models. Consequently, their tracking performance suffers. Additionally, these networks have a poor generalization ability and require re-training for the different spectral bands of hyperspectral data, leading to the inefficient use of computational resources. In order to address the aforementioned problems, we propose a spectral similarity prompt learning approach for hyperspectral object tracking (SPTrack). First, we introduce a spectral matching map based on spectral similarity, which converts 3D hyperspectral data with different spectral bands into single-channel hotmaps, thus enabling cross-spectral domain generalization. Then, we design a channel and position attention-based feature complementary prompter to learn blended prompts from spectral matching maps and three-channel images. Extensive experiments are conducted on the HOT2023 and IMEC25 data sets, and SPTrack is found to achieve state-of-the-art performance with minimal computational effort. Additionally, we verify the cross-spectral domain generalization ability of SPTrack on the HOT2023 data set, which includes data from three spectral bands. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking.
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Gaowei%22">Guo, Gaowei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guogaowei22@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhaoxu%22">Li, Zhaoxu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22An%2C+Wei%22">An, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yingqian%22">Wang, Yingqian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Xu%22">He, Xu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Yihang%22">Luo, Yihang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ling%2C+Qiang%22">Ling, Qiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lingqiang16@nudt.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Miao%22">Li, Miao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Zaiping%22">Lin, Zaiping</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2024, Vol. 16 Issue 16, p2975. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Design+templates%22">Design templates</searchLink><br /><searchLink fieldCode="DE" term="%22Generalization%22">Generalization</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink>
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  Data: Compared to hyperspectral trackers that adopt the "pre-training then fine-tuning" training paradigm, those using the "pre-training then prompt-tuning" training paradigm can inherit the expressive capabilities of the pre-trained model with fewer training parameters. Existing hyperspectral trackers utilizing prompt learning lack an adequate prompt template design, thus failing to bridge the domain gap between hyperspectral data and pre-trained models. Consequently, their tracking performance suffers. Additionally, these networks have a poor generalization ability and require re-training for the different spectral bands of hyperspectral data, leading to the inefficient use of computational resources. In order to address the aforementioned problems, we propose a spectral similarity prompt learning approach for hyperspectral object tracking (SPTrack). First, we introduce a spectral matching map based on spectral similarity, which converts 3D hyperspectral data with different spectral bands into single-channel hotmaps, thus enabling cross-spectral domain generalization. Then, we design a channel and position attention-based feature complementary prompter to learn blended prompts from spectral matching maps and three-channel images. Extensive experiments are conducted on the HOT2023 and IMEC25 data sets, and SPTrack is found to achieve state-of-the-art performance with minimal computational effort. Additionally, we verify the cross-spectral domain generalization ability of SPTrack on the HOT2023 data set, which includes data from three spectral bands. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Aug2024
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