Code and model for "Peeking into the Future: Predicting Future Person Activities and Locations in Videos", Liang et al, CVPR 2019

Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction

Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

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

STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction

State-of-the-art methods for human trajectory forecasting. Contains code for papers published at ECCV 2020 and ICCV 2021.

Code for our paper "MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction" published at ICCV 2021.

End-to-End Object Detection with Transformers

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Code for "Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans" https://arxiv.org/abs/2001.00735

Semantic Segmentation Architectures Implemented in PyTorch

ICNet implemented by pytorch, for real-time semantic segmentation on high-resolution images, mIOU=71.0 on cityscapes, single inference time is 19ms, FPS is 52.6.

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

A collection of algorithm pipelines for segmentation of aerial imagery implemented by PyTorch.

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

Jekyll plugin which generates a table of contents.

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset