Fine-tuning on same-model feature matching tasks. The X-FFN and Y-FFN represent the assistants of any two kinds of pre-trained different modal images in the second stage of Fig 4. The fine-tuning of the X-modal image and the fine-tuning of the Y-modal image are independent of each other.

<p>Fine-tuning on same-model feature matching tasks. The X-FFN and Y-FFN represent the assistants of any two kinds of pre-trained different modal images in the second stage of Fig 4. The fine-tuning of the X-modal image and the fine-tuning of the Y-modal image are independent of each other.<...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Yide Di (20969124) (author)
مؤلفون آخرون:	Yun Liao (160524) (author), Hao Zhou (136535) (author), Kaijun Zhu (18283913) (author), Qing Duan (541846) (author), Junhui Liu (2063140) (author), Mingyu Lu (2333083) (author)
منشور في:	2025
الموضوعات:	Sociology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified multimodal image applications image feature matching experimental results demonstrate data augmentation algorithm across different modals ufm exhibits versatility div >< p imbalanced modal datasets specific modals specific datasets sparse data wide spectrum ufm excels ufm </ trained model staged pre remains challenging modal images foundational task computer vision
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Fine-tuning on same-model feature matching tasks. The X-FFN and Y-FFN represent the assistants of any two kinds of pre-trained different modal images in the second stage of Fig 4. The fine-tuning of the X-modal image and the fine-tuning of the Y-modal image are independent of each other.

مواد مشابهة