We have collection of more than 1 Million open source products ranging from Enterprise product to
small libraries in all platforms. We aggregate information from all open source repositories.
Search and find the best for your needs. Check out projects section.
You shouldn't play video games all day, so shouldn't your AI! We built a virtual environment simulator, Gibson, that offers real-world experience for learning perception. I. being from the real-world and reflecting its semantic complexity through virtualizing real spaces, II. having a baked-in mechanism for transferring to real-world (Goggles function), and III. embodiment of the agent and making it subject to constraints of space and physics via integrating a physics engine (Bulletphysics).
We present a novel method to learn pedestrians' spatio-temporal patterns in unlabeled target datsets by transferring the visual classifier from the source dataset. The algorithm does not require any prior knowledge about the spatial distribution of cameras nor any assumption about how people move in the target environment. We propose a Bayesian fusion model, which combines the spatio-temporal patterns learned and the visual features to achieve high performance of person Re-ID in the unlabeled target datasets.
Decoupled Networks is released under the MIT License (refer to the LICENSE file for details). Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation that CNN-learned features are naturally decoupled with the norm of features corresponding to the intra-class variation and the angle corresponding to the semantic difference, we propose a generic decoupled learning framework which models the intra-class variation and semantic difference independently.
Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. Geometry-Aware Learning of Maps for Camera Localization. CVPR 2018..
Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). For Caffe users, please refer to Caffe/README.md.
This is initially described in our CVPR 2018 paper. After Caffe is set up, you need to download a trained model (about 40M) from Google Drive. This model is trained with VGG800k and finetuned on ICDAR2015.
This repository (https://github.com/twhui/LiteFlowNet) is the offical release of LiteFlowNet for my paper LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation in CVPR18 (Spotlight). The up-to-date version of the paper is available on arXiv. It comes as the modified Caffe from DispFlowNet and FlowNet2 with our new layers, scripts, and trained models.
This repo provides a TensorFlow-based implementation of the wonderful paper "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume," by Deqing Sun et al. (CVPR 2018). There are already a few attempts at implementing PWC-Net using TensorFlow out there. However, they either use outdated architectures of the paper's CNN networks, only provide TF inference (no TF training), only work on Linux platforms, and do not support multi-GPU training.
DBNet is a large-scale driving behavior dataset, which provides large-scale high-quality point clouds scanned by Velodyne lasers, high-resolution videos recorded by dashboard cameras and standard drivers' behaviors (vehicle speed, steering angle) collected by real-time sensors. Extensive experiments demonstrate that extra depth information helps networks to determine driving policies indeed. We hope it will become useful resources for the autonomous driving research community.