This project is an aid to the blind. Till date there has been no technological advancement in the way the blind navigate. So I have used deep learning particularly convolutional neural networks so that they can navigate through the streets.
The purpose of deep learning is to learn a representation of high dimensional and noisy data using a sequence of differentiable functions, i.e., geometric transformations, that can perhaps be used…
Interacting Systems Are Prevalent In Nature, From Dynamical Systems In Physics To Complex Societal Dynamics. The Interplay Of Components Can Give Rise To Complex Behavior, Which Can Often Be Explained Using A Simple Model Of The System's Constituent Parts. In This Work, We Introduce The Neural Relational Inference (nri) Model: An Unsupervised Model That Learns To Infer Interactions While Simultaneously Learning The Dynamics Purely From Observational Data. Our Model Takes The Form Of A Variational Auto-encoder, In Which The Latent Code Represents The Underlying Interaction Graph And The Reconstruction Is Based On Graph Neural Networks. In Experiments On Simulated Physical Systems, We Show That Our Nri Model Can Accurately Recover Ground-truth Interactions In An Unsupervised Manner. We Further Demonstrate That We Can Find An Interpretable Structure And Predict Complex Dynamics In Real Motion Capture And Sports Tracking Data.
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, und T. Funkhouser. (2016)cite arxiv:1603.08182Comment: To appear at the Conference on Computer Vision and Pattern Recognition (CVPR) 2017. Project webpage: http://3dmatch.cs.princeton.edu.
A. Mousavian, D. Anguelov, J. Flynn, und J. Kosecka. (2016)cite arxiv:1612.00496Comment: To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017.
S. Levine, P. Pastor, A. Krizhevsky, und D. Quillen. (2016)cite arxiv:1603.02199Comment: This is an extended version of "Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection," ISER 2016. Draft modified to correct typo in Algorithm 1 and add a link to the publicly available dataset.
H. Lin, M. Tegmark, und D. Rolnick. (2016)cite arxiv:1608.08225Comment: Replaced to match version published in Journal of Statistical Physics: https://link.springer.com/article/10.1007/s10955-017-1836-5 Improved refs & discussion, typos fixed. 16 pages, 3 figs.
A. Boulch, und R. Marlet. Proceedings of the Symposium on Geometry Processing, Seite 281--290. Goslar Germany, Germany, Eurographics Association, (2016)
P. Huang, K. Matzen, J. Kopf, N. Ahuja, und J. Huang. (2018)cite arxiv:1804.00650Comment: CVPR 2018. Project page: https://phuang17.github.io/DeepMVS/ Code: https://github.com/phuang17/DeepMVS.
K. Zhang, M. Sun, T. Han, X. Yuan, L. Guo, und T. Liu. (2016)cite arxiv:1608.02908Comment: IEEE Transactions on Circuits and Systems for Video Technology 2017.
V. Patraucean, A. Handa, und R. Cipolla. (2015)cite arxiv:1511.06309Comment: The experiments section has been extended and a direct application to weakly-supervised video segmentation through label propagation has been included.