pytorch implemention of trajGRU.

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

Video Predicting using ConvLSTM and pytorch

Using the Pytorch to build an image temporal prediction model of the encoder-forecaster structure, ConvGRU kernel & ConvLSTM kernel

Implementation of ConvLSTM in pytorch applied for BCI (Brain Machine Interface) following paper: Convolutional LSTM Network-A Machine Learning Approach for Precipitation Nowcasting

雷达回波外推，ConvLSTM，训练模型并外推。

TianChi AIEarth Contest Solution

Pytorch implementations of ConvLSTM and ConvGRU modules with examples

A model for short-term precipitation forecasting based on radar data

[TGRS 2020] The official repo for the paper "Adaptive Spectral-Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification".

GAN based ConvLSTM in precipitation prediction (Trial Version)

Efficient violence detection in surveillance videos using Human Skeletons and Motion Estimation

This repo contains an implementation code for the weakly supervised surgical tool tracker. In this research, the temporal dependency in surgical video data is modeled using a convolutional LSTM which is trained only on image level labels to detect, localize and track surgical instruments.

Application of LSTM network for Structural Health Monitoring & Non-Destructive Testing

Physics-informed deep super-resolution of spatiotemporal data

Images timeseries sequence with ConvLSTM for windspeed prediction & CNN cyclone intensity

Using DDPG and ConvLSTM to control a drone to avoid obstacle in AirSim

[Pattern Recognition]Video Saliency Prediction using Enhanced Spatiotemporal Alignment Network

In this repository, we focus on video frame prediction the task of predicting future frames given a set of past frames. We present an Adversarial Spatio-Temporal Convolutional LSTM architecture to predict the future frames of the Moving MNIST Dataset. We evaluate the model on long-term future frame prediction and its performance of the model on …

Performed various Deep Learning techniques to detect Human Activity using Sequential Data detect human activities generated by sensor-based wearable devices