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d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。

datasetsscenes

Unity representetion of UCY and ETH datasets scenes

keras-sru

Implementation of Simple Recurrent Unit (SRU) using Keras and Recurrentshop

keras-sru-1

Implementation of Simple Recurrent Unit in Keras

learning-pytorch-using-deep-learning

learning_pytorch_with_sherlockliao

廖星宇《深度学习入门之PyTorch》代码实践

orb-slam2_rgbd_dense_map

This repository is modified from Xiang Gao's "ORB_SLAM2_modified".It is added a dense loopclosing map model.

orb_slam2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

orb_slam2_detailed_comments

Detailed comments for ORB-SLAM2 with trouble-shooting, key formula derivation, and diagrammatic drawing

orbslam2_with_pointcloud_map

sgan

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

slambook

social-lstm

Social LSTM implementation in PyTorch

social_lstm_pedestrian_prediction

The aim of the project is to predict the trajectories of pedestrians using lstm neural networks. The project starts from the paper "Social LSTM: Human Trajectory Prediction in Crowded Spaces - Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese - Stanford University - The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 961-971", and its official implementation (https://github.com/vvanirudh/social-lstm-tf) and makes some modifications. This is the Multimedia communication course project made during my final year of the bachelor degree under the supervision of professor Nicola Conci and his phd student Niccolò Bisagno. The modifications introduced are two: - To every simulated pedestrian add the input goal; the goal is the final position (in x and y coordinates) of that pedestrian when it disappears from the video. This modification should improve the predicted trajectory of that pedestrian because of the introduction of this new information - The grid created for every pedestrian in the original project to identify nearby pedestrians is replaced with an array containing the position(in x and y coordinates) of the others pedestrians in distance order, from the closest to the farther. This modification should improve the model results beacuse it presents relevant informations in order to the neural network. Then these two modifications were combined in to a single model. Every model has been evaluated in the test videos with different parameters and in conclusion the model with the two modifications (goal and array) combined performed better than any other model. Also the two modify models performed better than the original model. These results can be seen in the report at page 12 and 13. Unfortunately I haven't the time to translate the report in english, because now is in italian, but the result table at page 12 an 13 should be pretty clear. Technical details: - Programming language: Python 2.7 - Neural networks library used: Tensorflow 1.5 - External libraries: CUDA 8.0, CUDNN 6.0 - OS: Linux, Ubuntu 16.04 distrubution License: GPL v3

socialways

Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs

sr-lstm

States Refinement LSTM

sru-tensorflow

Training RNNs as fast as CNNs. An unofficial tensorflow implementation.

ss-ssr-v2ray

ssr/v2ray订阅机场推荐与评测2022

starfyx.github.io

time-series-prediction-with-gan

stock forecasting with sentiment variables(with lstm as generator and mlp as discriminator)

visual-of-social-gan

Show Visualization Result of Social GAN