Pulse Contributors Commits Code frequency Dependency graph Network Forks Forks switch to list view jbhuang0604 / awesome-computer-vision 07216 / awesome-computer-vision 0xaguspunk / awesome-computer-vision 0XFC-LL / awesome-computer-vision 2torus / awesome-computer-vision 307509256 / awesom...
U-Net Med TensorFlow2 Yes Yes - Example - Supported Yes - Natural Language Processing ModelsFrameworkAMPMulti-GPUMulti-NodeTensorRTONNXTritonDLCNB BERT PyTorch Yes Yes Yes Example - Example Yes - GNMT PyTorch Yes Yes - Supported - Supported - - ELECTRA TensorFlow2 Yes Yes Yes Supported - Supp...
Pulse Contributors Commits Code frequency Dependency graph Network Forks Woah, this network is huge! We’re showing only some of this network’s repositories. Forks of peakvision/shadowsocks-rssshadowsocksr-backup / shadowsocks-rss 080900 / shadowsocks-rss 0x-newe / shadowsocks-rss 0x4e38 ...
(2019). Language-conditioned graph networks for relational reasoning. arXiv preprint arXiv:1905.04405. Hudson, D. A., & Manning, C. D. (2018). Compositional attention networks for machine reasoning. arXiv preprint arXiv:1803.03067. Ishikawa, T., & Nakamura, U. (2012). Landmark selection ...
Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling ...
Spatial-Temporal Transformer for Dynamic Scene Graph Generation [paper] [GLiT] GLiT: Neural Architecture Search for Global and Local Image Transformer [paper] [TRAR] TRAR: Routing the Attention Spans in Transformer for Visual Question Answering [paper] [UniT] UniT: Multimodal Multitask Learning Wit...
If attempting to recall positive memories the brain has to work hard as indicated by an significantly higher amplitudes of beta2, beta3, and beta4 EEG (i.e., electroencephalograph) when sitting slouched then when sitting upright (Tsai et al., 2016). ...
Ghorbani M, Kazi A, Baghshah MS, Rabiee HR, Navab N (2022) Ra-gcn: graph convolutional network for disease prediction problems with imbalanced data. Med Image Anal 75:102272 Article Google Scholar Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image...
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing, arXiv:1910.03579, 2019. Zhu, A., Wang, Z., Khant, K., Daniilidis, K., EventGAN: Leveraging Large Scale Image Datasets for Event Cameras, arXiv:1912.01584, 2019. Cannici, M., Ciccone, M., Romanoni, A., Mat...
Efficient hierarchical graph-based video segmentation Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2010 Jun 13–18; San Francisco, CA, USA, IEEE, Piscataway (2010), pp. 2141-2148 CrossrefView in ScopusGoogle Scholar [55] N. Wadhwa, M. Rubinste...