Zobrazeno 1 - 10
of 1 636
pro vyhledávání: '"Wang, Yaonan"'
Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness o
Externí odkaz:
http://arxiv.org/abs/2411.09924
Quantum machine learning is considered one of the current research fields with immense potential. In recent years, Havl\'i\v{c}ek et al. [Nature 567, 209-212 (2019)] have proposed a quantum machine learning algorithm with quantum-enhanced feature spa
Externí odkaz:
http://arxiv.org/abs/2411.02913
Autor:
Wang, Jiacheng, Chen, Xiang, Hu, Renjiu, Wang, Rongguang, Liu, Min, Wang, Yaonan, Wang, Jiazheng, Li, Hao, Zhang, Hang
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues. However, large discrepancies
Externí odkaz:
http://arxiv.org/abs/2410.20812
Surgical navigation based on multimodal image registration has played a significant role in providing intraoperative guidance to surgeons by showing the relative position of the target area to critical anatomical structures during surgery. However, d
Externí odkaz:
http://arxiv.org/abs/2409.05040
Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model t
Externí odkaz:
http://arxiv.org/abs/2409.04208
Autor:
Zhang, Yuxi, Chen, Xiang, Wang, Jiazheng, Liu, Min, Wang, Yaonan, Liu, Dongdong, Hu, Renjiu, Zhang, Hang
In this paper, we summarize the methods and experimental results we proposed for Task 2 in the learn2reg 2024 Challenge. This task focuses on unsupervised registration of anatomical structures in brain MRI images between different patients. The diffi
Externí odkaz:
http://arxiv.org/abs/2409.00917
In recent years, the rapid expansion of model sizes has led to large-scale pre-trained models demonstrating remarkable capabilities. Consequently, there has been a trend towards increasing the scale of models. However, this trend introduces significa
Externí odkaz:
http://arxiv.org/abs/2408.14961
Autor:
Yang, Fan, Chen, Wenrui, Yang, Kailun, Lin, Haoran, Luo, DongSheng, Tang, Conghui, Li, Zhiyong, Wang, Yaonan
To enable robots to use tools, the initial step is teaching robots to employ dexterous gestures for touching specific areas precisely where tasks are performed. Affordance features of objects serve as a bridge in the functional interaction between ag
Externí odkaz:
http://arxiv.org/abs/2407.00614
Autor:
Yi, Junfei, Mao, Jianxu, Liu, Tengfei, Li, Mingjie, Gu, Hanyu, Zhang, Hui, Chang, Xiaojun, Wang, Yaonan
Knowledge distillation (KD) is a widely adopted and effective method for compressing models in object detection tasks. Particularly, feature-based distillation methods have shown remarkable performance. Existing approaches often ignore the uncertaint
Externí odkaz:
http://arxiv.org/abs/2406.06999
Autor:
Lu, Yang, Yao, Weijia, Xiao, Yongqian, Zhang, Xinglong, Xu, Xin, Wang, Yaonan, Xiao, Dingbang
In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding vector fields (GVFs) may limit the motion capability due to the improper
Externí odkaz:
http://arxiv.org/abs/2405.08283