Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Papandreou, George"'
Autor:
Lê, Eric-Tuan, Kakolyris, Antonis, Koutras, Petros, Tam, Himmy, Skordos, Efstratios, Papandreou, George, Güler, Rıza Alp, Kokkinos, Iasonas
Publikováno v:
CVPR 2024
DensePose provides a pixel-accurate association of images with 3D mesh coordinates, but does not provide a 3D mesh, while Human Mesh Reconstruction (HMR) systems have high 2D reprojection error, as measured by DensePose localization metrics. In this
Externí odkaz:
http://arxiv.org/abs/2406.10180
Autor:
Pandey, Rohit, Tkach, Anastasia, Yang, Shuoran, Pidlypenskyi, Pavel, Taylor, Jonathan, Martin-Brualla, Ricardo, Tagliasacchi, Andrea, Papandreou, George, Davidson, Philip, Keskin, Cem, Izadi, Shahram, Fanello, Sean
Volumetric (4D) performance capture is fundamental for AR/VR content generation. Whereas previous work in 4D performance capture has shown impressive results in studio settings, the technology is still far from being accessible to a typical consumer
Externí odkaz:
http://arxiv.org/abs/1905.12162
Autor:
Yang, Tien-Ju, Collins, Maxwell D., Zhu, Yukun, Hwang, Jyh-Jing, Liu, Ting, Zhang, Xiao, Sze, Vivienne, Papandreou, George, Chen, Liang-Chieh
We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for 'stuff' classes and instance segmentation for 'thing' classes, assignin
Externí odkaz:
http://arxiv.org/abs/1902.05093
Autor:
Chen, Liang-Chieh, Collins, Maxwell D., Zhu, Yukun, Papandreou, George, Zoph, Barret, Schroff, Florian, Adam, Hartwig, Shlens, Jonathon
The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically thro
Externí odkaz:
http://arxiv.org/abs/1809.04184
Autor:
Papandreou, George, Zhu, Tyler, Chen, Liang-Chieh, Gidaris, Spyros, Tompson, Jonathan, Murphy, Kevin
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-p
Externí odkaz:
http://arxiv.org/abs/1803.08225
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or poo
Externí odkaz:
http://arxiv.org/abs/1802.02611
Autor:
Chen, Liang-Chieh, Hermans, Alexander, Papandreou, George, Schroff, Florian, Wang, Peng, Adam, Hartwig
In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic
Externí odkaz:
http://arxiv.org/abs/1712.04837
In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segme
Externí odkaz:
http://arxiv.org/abs/1706.05587
Autor:
Papandreou, George, Zhu, Tyler, Kanazawa, Nori, Toshev, Alexander, Tompson, Jonathan, Bregler, Chris, Murphy, Kevin
We propose a method for multi-person detection and 2-D pose estimation that achieves state-of-art results on the challenging COCO keypoints task. It is a simple, yet powerful, top-down approach consisting of two stages. In the first stage, we predict
Externí odkaz:
http://arxiv.org/abs/1701.01779
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous
Externí odkaz:
http://arxiv.org/abs/1606.00915