Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection

Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware Semi-Supervised Transformer framework designed to achieve...

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書誌詳細

主要な著者:	Sabina Umirzakova, Shakhnoza Muksimova, Abrayeva Mahliyo Olimjon Qizi, Young Im Cho
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2025-05-01
シリーズ:	Applied Sciences
主題:	lightweight vision transformer oriented object detection semi-supervised learning rotational invariance pseudo-labeling adaptive rotational convolution
オンライン･アクセス:	https://www.mdpi.com/2076-3417/15/9/5212
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