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pro vyhledávání: '"Wang, Weijie"'
The challenge of Multimodal Deformable Image Registration (MDIR) lies in the conversion and alignment of features between images of different modalities. Generative models (GMs) cannot retain the necessary information enough from the source modality
Externí odkaz:
http://arxiv.org/abs/2408.10703
Autor:
Kudyba, Paul, Mandapaka, Jaya Sravani, Wang, Weijie, McCorkendale, Logan, McCorkendale, Zachary, Kidane, Mathias, Sun, Haijian, Adams, Eric, Namuduri, Kamesh, Fund, Fraida, Sichitiu, Mihail, Ozdemir, Ozgur
As wireless researchers are tasked to enable wireless communication as infrastructure in more dynamic aerial settings, there is a growing need for large-scale experimental platforms that provide realistic, reproducible, and reliable experimental vali
Externí odkaz:
http://arxiv.org/abs/2407.12180
Autor:
Ren, Bin, Mei, Guofeng, Paudel, Danda Pani, Wang, Weijie, Li, Yawei, Liu, Mengyuan, Cucchiara, Rita, Van Gool, Luc, Sebe, Nicu
Contrastive learning (CL) for Vision Transformers (ViTs) in image domains has achieved performance comparable to CL for traditional convolutional backbones. However, in 3D point cloud pretraining with ViTs, masked autoencoder (MAE) modeling remains d
Externí odkaz:
http://arxiv.org/abs/2407.05862
Enabling robots to autonomously perform hybrid motions in diverse environments can be beneficial for long-horizon tasks such as material handling, household chores, and work assistance. This requires extensive exploitation of intrinsic motion capabil
Externí odkaz:
http://arxiv.org/abs/2406.14655
Autor:
Feng, Kehua, Ding, Keyan, Wang, Weijie, Zhuang, Xiang, Wang, Zeyuan, Qin, Ming, Zhao, Yu, Yao, Jianhua, Zhang, Qiang, Chen, Huajun
Large language models (LLMs) have gained increasing prominence in scientific research, but there is a lack of comprehensive benchmarks to fully evaluate their proficiency in understanding and mastering scientific knowledge. To address this need, we i
Externí odkaz:
http://arxiv.org/abs/2406.09098
Autor:
Wang, Weijie, Zhang, Jichao, Liu, Chang, Li, Xia, Xu, Xingqian, Shi, Humphrey, Sebe, Nicu, Lepri, Bruno
Recently, diffusion models have made significant strides in synthesizing realistic 2D human images based on provided text prompts. Building upon this, researchers have extended 2D text-to-image diffusion models into the 3D domain for generating human
Externí odkaz:
http://arxiv.org/abs/2404.14568
In this paper, a class of high-order methods to numerically solve Functional Differential Equations with Piecewise Continuous Arguments (FDEPCAs) is discussed. The framework stems from the expansion of the vector field associated with the reference d
Externí odkaz:
http://arxiv.org/abs/2403.08597
Autor:
Yan, Yunzhi, Lin, Haotong, Zhou, Chenxu, Wang, Weijie, Sun, Haiyang, Zhan, Kun, Lang, Xianpeng, Zhou, Xiaowei, Peng, Sida
This paper aims to tackle the problem of modeling dynamic urban streets for autonomous driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban str
Externí odkaz:
http://arxiv.org/abs/2401.01339
Autor:
Wang, Weijie, Mei, Guofeng, Ren, Bin, Huang, Xiaoshui, Poiesi, Fabio, Van Gool, Luc, Sebe, Nicu, Lepri, Bruno
Learning-based point cloud registration approaches have significantly outperformed their traditional counterparts. However, they typically require extensive training on specific datasets. In this paper, we propose , the first zero-shot point cloud re
Externí odkaz:
http://arxiv.org/abs/2312.03032
Malicious use of deepfakes leads to serious public concerns and reduces people's trust in digital media. Although effective deepfake detectors have been proposed, they are substantially vulnerable to adversarial attacks. To evaluate the detector's ro
Externí odkaz:
http://arxiv.org/abs/2309.01104