Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation

Recognizing objects in low-resolution images is a challenging task due to the lack of informative details. Recent studies have shown that knowledge distillation approaches can effectively transfer knowledge from a high-resolution teacher model to a low-resolution student model by aligning cross-reso...

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Veröffentlicht in:IEEE transactions on circuits and systems for video technology 2024-04, Vol.34 (4), p.1-1
Hauptverfasser: Zhang, Kangkai, Ge, Shiming, Shi, Ruixin, Zeng, Dan
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container_title IEEE transactions on circuits and systems for video technology
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creator Zhang, Kangkai
Ge, Shiming
Shi, Ruixin
Zeng, Dan
description Recognizing objects in low-resolution images is a challenging task due to the lack of informative details. Recent studies have shown that knowledge distillation approaches can effectively transfer knowledge from a high-resolution teacher model to a low-resolution student model by aligning cross-resolution representations. However, these approaches still face limitations in adapting to the situation where the recognized objects exhibit significant representation discrepancies between training and testing images. In this study, we propose a cross-resolution relational contrastive distillation approach to facilitate low-resolution object recognition. Our approach enables the student model to mimic the behavior of a well-trained teacher model which delivers high accuracy in identifying high-resolution objects. To extract sufficient knowledge, the student learning is supervised with contrastive relational distillation loss, which preserves the similarities in various relational structures in contrastive representation space. In this manner, the capability of recovering missing details of familiar low-resolution objects can be effectively enhanced, leading to a better knowledge transfer. Extensive experiments on low-resolution object classification and low-resolution face recognition clearly demonstrate the effectiveness and adaptability of our approach.
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subjects Adaptation models
Distillation
domain adaptation
Face recognition
Germanium
High resolution
Image resolution
knowledge distillation
Knowledge management
Knowledge transfer
Low-resolution face recognition
low-resolution object classification
Object recognition
Representations
Teachers
Training
Visualization
title Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation
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