Canyu Mo — accepted AI-conference papers

2026

ORSATR-X: A Foundation Model based on Differential-and-Excitation Networks for Optical Remote Sensing Object Recognition

Canyu Mo, Yongxiang Liu, Jiehua Zhang, Zilong Yu, Zhen Liu, Tianpeng Liu +1

CVPR 2026

Recent advances in Remote Sensing Foundation Models (RSFMs) have demonstrated considerable potential for Earth Observation (EO) tasks. While adopting natural image foundation models (e.g., DINO) provides a data-efficient strategy for building RSFMs, their strong generalization capability does not fu

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2025

When Pixel Difference Patterns Meet ViT: PiDiViT for Few-Shot Object Detection

Hongliang Zhou, Yongxiang Liu, Canyu Mo, Weijie Li, Bowen Peng, Li Liu

ICCV 2025poster

Few-shot object detection aims to detect novel classes with limited samples. Recent methods have leveraged the rich semantic representations of pretrained vision transformer (ViT) to overcome the limitations of model fine-tuning, thereby improving the performance on novel classes. However, existing…

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