[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
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Updated
Dec 8, 2023 - Python
[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
SiamMOT: Siamese Multi-Object Tracking
Official implementation of Paper Future Frame Prediction for Anomaly Detection -- A New Baseline, CVPR 2018
Video-based object counting software.
Code release for ActionFormer (ECCV 2022)
Video and Image Analytics for Multiple Environments
Codebase for CVPR2020 A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
[NeurIPS 2021 Spotlight] Official implementation of Long Short-Term Transformer for Online Action Detection
Useful Toolbox for Anomaly Detection
An official TensorFlow implementation of "Neural Program Synthesis from Diverse Demonstration Videos" (ICML 2018) by Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, and Joseph J. Lim
To keep updates with VRU Grand Challenge, please use https://github.com/NExTplusplus/VidVRD-helper
A short script showing how to build simple real-time video analytics apps using YOLOv8 and Supervision. Try it out, and most importantly have fun! 🤪
[MM'21] Former-DFER: Dynamic Facial Expression Recognition Transformer
Analyzing CCTV videos to find interesting events
Official Tensorflow Implementation of the AAAI-2020 paper "Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction"
Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video (AAAI2020)
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.
Code to detect scenes and transitions in videos and compose a video to visualize the data.
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