Zobrazeno 1 - 10
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pro vyhledávání: '"Shi Guangming"'
Autor:
Feng, Jie, Zhang, Tianshu, Zhang, Junpeng, Shang, Ronghua, Dong, Weisheng, Shi, Guangming, Jiao, Licheng
Unsupervised domain adaptation techniques, extensively studied in hyperspectral image (HSI) classification, aim to use labeled source domain data and unlabeled target domain data to learn domain invariant features for cross-scene classification. Comp
Externí odkaz:
http://arxiv.org/abs/2408.15263
Autor:
Ma, Shuai, Zhang, Chuanhui, Shen, Bin, Wu, Youlong, Li, Hang, Li, Shiyin, Shi, Guangming, Al-Dhahir, Naofal
With the ever-increasing user density and quality of service (QoS) demand,5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature divis
Externí odkaz:
http://arxiv.org/abs/2407.08424
Back to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth Estimation
Autor:
Zhu, Yufan, Ran, Chongzhi, Feng, Mingtao, Wu, Fangfang, Dong, Le, Dong, Weisheng, López, Antonio M., Shi, Guangming
Virtual engines can generate dense depth maps for various synthetic scenes, making them invaluable for training depth estimation models. However, discrepancies between synthetic and real-world colors pose significant challenges for depth estimation i
Externí odkaz:
http://arxiv.org/abs/2406.07741
Autor:
Huang, Yipo, Sheng, Xiangfei, Yang, Zhichao, Yuan, Quan, Duan, Zhichao, Chen, Pengfei, Li, Leida, Lin, Weisi, Shi, Guangming
The highly abstract nature of image aesthetics perception (IAP) poses significant challenge for current multimodal large language models (MLLMs). The lack of human-annotated multi-modality aesthetic data further exacerbates this dilemma, resulting in
Externí odkaz:
http://arxiv.org/abs/2404.09624
Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In this paper, we propose window-based event denoising, which simultaneous
Externí odkaz:
http://arxiv.org/abs/2402.09270
Autor:
Liu, Jiarun, Yang, Hao, Zhou, Hong-Yu, Xi, Yan, Yu, Lequan, Yu, Yizhou, Liang, Yong, Shi, Guangming, Zhang, Shaoting, Zheng, Hairong, Wang, Shanshan
Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies. However, it is challenging for existing methods to model long-range global information, where convolutional n
Externí odkaz:
http://arxiv.org/abs/2402.03302
Current communication technologies face limitations in terms of theoretical capacity, spectrum availability, and power resources. Pragmatic communication, leveraging terminal intelligence for selective data transmission, offers resource conservation.
Externí odkaz:
http://arxiv.org/abs/2402.01750
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D neural calibration, a novel pretext task that estimates the rigid pos
Externí odkaz:
http://arxiv.org/abs/2401.12452
Autor:
Huang, Weijian, Li, Cheng, Zhou, Hong-Yu, Liu, Jiarun, Yang, Hao, Liang, Yong, Shi, Guangming, Zheng, Hairong, Wang, Shanshan
The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications. While previous studies have commonly focused on
Externí odkaz:
http://arxiv.org/abs/2401.01583
Autor:
Yang, Hao, Zhou, Hong-Yu, Li, Cheng, Huang, Weijian, Liu, Jiarun, Liang, Yong, Shi, Guangming, Zheng, Hairong, Liu, Qiegen, Wang, Shanshan
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation in
Externí odkaz:
http://arxiv.org/abs/2401.01524