Awesome Groundingawesome grounding: A curated list of research papers in visual grounding
PaddlevideoComprehensive, latest, and deployable video deep learning algorithm, including video recognition, action localization, and temporal action detection tasks. It's a high-performance, light-weight codebase provides practical models for video understanding research and application
ActionvladActionVLAD for video action classification (CVPR 2017)
StepSTEP: Spatio-Temporal Progressive Learning for Video Action Detection. CVPR'19 (Oral)
Youtube 8mThe 2nd place Solution to the Youtube-8M Video Understanding Challenge by Team Monkeytyping (based on tensorflow)
Object level visual reasoningPytorch Implementation of "Object level Visual Reasoning in Videos", F. Baradel, N. Neverova, C. Wolf, J. Mille, G. Mori , ECCV 2018
Video2tfrecordEasily convert RGB video data (e.g. .avi) to the TensorFlow tfrecords file format for training e.g. a NN in TensorFlow. This implementation allows to limit the number of frames per video to be stored in the tfrecords.
MultiverseDataset, code and model for the CVPR'20 paper "The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction". And for the ECCV'20 SimAug paper.
MmactionAn open-source toolbox for action understanding based on PyTorch
I3d finetuneTensorFlow code for finetuning I3D model on UCF101.
Tdn[CVPR 2021] TDN: Temporal Difference Networks for Efficient Action Recognition
Mmaction2OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
DEAR[ICCV 2021 Oral] Deep Evidential Action Recognition
PyAnomalyUseful Toolbox for Anomaly Detection
DIN-Group-Activity-Recognition-BenchmarkA new codebase for Group Activity Recognition. It contains codes for ICCV 2021 paper: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition and some other methods.
just-ask[TPAMI Special Issue on ICCV 2021 Best Papers, Oral] Just Ask: Learning to Answer Questions from Millions of Narrated Videos
STCNetSTCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection
MTL-AQAWhat and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment [CVPR 2019]
NExT-QANExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR'21)
glimpse cloudsPytorch implementation of the paper "Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points", F. Baradel, C. Wolf, J. Mille , G.W. Taylor, CVPR 2018
SSTDA[CVPR 2020] Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation (PyTorch)