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Repository for the paper "Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition"
[CVPR 2022] Official Pytorch Implementation for "Spatio-temporal Relation Modeling for Few-shot Action Recognition". SOTA Results for Few-shot Action Recognition
[TCSVT23, Highly Cited Paper] Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment
Official PyTorch Repository of "Task Discrepancy Maximization for Fine-grained Few-Shot Classification" (TDM, CVPR 2022 Oral Paper)
Code release for Bi-Directional Feature Reconstruction Network for Fine-grained Few-shot Image Classification
The summary of code and paper for few-shot learning in fine-grained recognition
Official codes for CVPR2021 paper "MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection"
An official implementation of "Learning Memory-guided Normality for Anomaly Detection" (CVPR 2020) in PyTorch.
Official codes of CVPR21 paper: Learning Normal Dynamics in Videos with Meta Prototype Network
Appearance-Motion Memory Consistency Network for Video Anomaly Detection
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video
Official code for 'Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning' [ICCV 2021]
[CVPR 2023] Official code for paper: Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
Structure tensor 2D and 3D implementation for Python.
The source code for our CVPR 2016 work "Slicing Convolutional Neural Network for Crowd Video Understanding".
Background subtraction algorithm by Gaussian Mixture Model based on paper "Adaptive background mixture models for real-time tracking".
We developed a python UI based on labelme and segment-anything for pixel-level annotation. It support multiple masks generation by SAM(box/point prompt), efficient polygon modification and category…
A C++ Background Subtraction Library with wrappers for Python, MATLAB, Java and GUI on QT
moving object detection for satellite videos.