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pro vyhledávání: '"Ward, Rabab"'
The increasing demand for medical imaging has surpassed the capacity of available radiologists, leading to diagnostic delays and potential misdiagnoses. Artificial intelligence (AI) techniques, particularly in automatic medical report generation (AMR
Externí odkaz:
http://arxiv.org/abs/2408.13988
This paper proposes an end-to-end framework for generating 3D human pose datasets using Neural Radiance Fields (NeRF). Public datasets generally have limited diversity in terms of human poses and camera viewpoints, largely due to the resource-intensi
Externí odkaz:
http://arxiv.org/abs/2312.14915
Modern autonomous vehicles rely heavily on mechanical LiDARs for perception. Current perception methods generally require 360{\deg} point clouds, collected sequentially as the LiDAR scans the azimuth and acquires consecutive wedge-shaped slices. The
Externí odkaz:
http://arxiv.org/abs/2209.04966
This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by
Externí odkaz:
http://arxiv.org/abs/2112.11593
Autor:
Tahir, Anas Mohammed, Guo, Li, Ward, Rabab K., Yu, Xinhui, Rideout, Andrew, Hore, Michael, Wang, Z. Jane
Publikováno v:
In Computers in Biology and Medicine October 2024 181
Estimating 3D human poses from video is a challenging problem. The lack of 3D human pose annotations is a major obstacle for supervised training and for generalization to unseen datasets. In this work, we address this problem by proposing a weakly-su
Externí odkaz:
http://arxiv.org/abs/2105.06599
Autor:
Wang, Dan, Cui, Xinrui, Chen, Xun, Zou, Zhengxia, Shi, Tianyang, Salcudean, Septimiu, Wang, Z. Jane, Ward, Rabab
Deep CNN-based methods have so far achieved the state of the art results in multi-view 3D object reconstruction. Despite the considerable progress, the two core modules of these methods - multi-view feature extraction and fusion, are usually investig
Externí odkaz:
http://arxiv.org/abs/2103.12957
Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception. Using these point cloud and image data, perception models based on deep neural nets (DNNs) have achieved state-of-the-art performance in 3D detection. The vulnerabili
Externí odkaz:
http://arxiv.org/abs/2103.09448
Autor:
Karimi, Davood, Ward, Rabab K.
As the medical usage of computed tomography (CT) continues to grow, the radiation dose should remain at a low level to reduce the health risks. Therefore, there is an increasing need for algorithms that can reconstruct high-quality images from low-do
Externí odkaz:
http://arxiv.org/abs/2103.03968
Publikováno v:
2021 IEEE International Conference on Image Processing (ICIP)
We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. DNNs have achieved high performance in 3D object detection, but they are known to be vulne
Externí odkaz:
http://arxiv.org/abs/2101.10747