sofiabroome

sofiabroome's Stars

• HarisIqbal88/PlotNeuralNet

  Latex code for making neural networks diagrams

  Language:TeX22.1k2261222.9k

• open-mmlab/mmaction2

  OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

  Language:Python4.3k421.4k1.2k

• kenshohara/3D-ResNets-PyTorch

  3D ResNets for Action Recognition (CVPR 2018)

  Language:Python3.9k58269930

• ndrplz/ConvLSTM_pytorch

  Implementation of Convolutional LSTM in PyTorch.

  Language:Python2k2029434

• deepmind/kinetics-i3d

  Convolutional neural network model for video classification trained on the Kinetics dataset.

  Language:Python1.7k57119463

• facebookresearch/TimeSformer

  The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

  Language:Python1.6k27129212

• Lightning-AI/deep-learning-project-template

  Pytorch Lightning code guideline for conferences

  Language:Python1.2k1816273

• lucidrains/TimeSformer-pytorch

  Implementation of TimeSformer from Facebook AI, a pure attention-based solution for video classification

  Language:Python691171886

• hkchengrex/STCN

  [NeurIPS 2021] Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

  Language:Python543915969

• ShirAmir/dino-vit-features

  Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".

  Language:Python39142145

• osai-ai/tensor-stream

  A library for real-time video stream decoding to CUDA memory

  Language:C++380163144

• adityac94/Grad_CAM_plus_plus

  A generalized gradient-based CNN visualization technique

  Language:Python288101556

• HaohanWang/ImageNet-Sketch

  ImageNet-Sketch data set for evaluating model's ability in learning (out-of-domain) semantics at ImageNet scale

  Language:Python2034516

• joaanna/something_else

  Code repository for the paper: 'Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks'

  Language:Python14562214

• baldassarreFe/graph-network-explainability

  Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

  Language:Jupyter Notebook1207416

• adebayoj/sanity_checks_saliency

  Language:Jupyter Notebook10810412

• GestureGeneration/Speech_driven_gesture_generation_with_autoencoder

  This is the official implementation for IVA '19 paper "Analyzing Input and Output Representations for Speech-Driven Gesture Generation".

  Language:Python10781027

• Chuhanxx/Temporal_Query_Networks

  The implementation of CVPR2021 paper Temporal Query Networks for Fine-grained Video Understanding

  Language:Python62266

• Svito-zar/speech-driven-hand-gesture-generation-demo

  This repository contains the gesture generation model from the paper "Moving Fast and Slow" (https://www.tandfonline.com/doi/full/10.1080/10447318.2021.1883883) trained on the English dataset

  Language:Python25275

• m-bain/video-transformers

  Implementations of Transformers for Video

  Language:Python24401

• thuml/VideoDG

  Language:Python20463

• gaow0007/Fast-Multi-Video-Style-Transfer

  Language:Python19316

• menorashid/action_graphs

  Action Graphs: Weakly-supervised Action Localization with Graph Convolution Networks

  Language:Python17323

• jonasrothfuss/DeepEpisodicMemory

  Deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experience - Research Project at KIT's High Performance Humanoids Technologies Lab (H2T)

  Language:Python14415

• pgalatic/fast-artistic-videos-pytorch

  Fast Artistic Videos in pyTorch

  Language:Python133

• interpreting-video-features/interpreting-video-features

  Code for the paper "Interpreting video features: A comparison of 3D Convolutional networks and Convolutional LSTM networks"

  Language:Python9400

• lilfelix/reproducibility_NeurIPS19

  Language:Python9301

• Yunbo426/e3d_lstm

  deep learning; video understanding; unsupervised learning

  Language:Python9106

• ZhenghLi/Automated-Detection-of-Equine-Facial-Action-Units

  Automated Detection of Equine Facial Action Units

  Language:Python51