Normal Learning in Videos with Attention Prototype Network

Related tags

Deep LearningAPN
Overview

Codes_APN

Official codes of CVPR21 paper: Normal Learning in Videos with Attention Prototype Network (https://arxiv.org/abs/2108.11055)

Overview of our approach based on APU and CAU model:

Introduction

Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection. With models trained on the normal data, the reconstruction errors of anomalous scenes are usually much larger than those of normal ones. Previous methods introduced the memory bank into AE, for encoding diverse normal patterns across the training videos. However, they are memory consuming and cannot cope with unseen new scenarios in the testing data. In this work, we propose a self-attention prototype unit (APU) to encode the normal latent space as prototypes in real time, free from extra memory cost. In addition, we introduce circulative attention mechanism to our backbone to form a novel feature extracting learner, namely Circulative Attention Unit(CAU). It enables the fast adaption capability on new scenes by only consuming a few iterations of update. Extensive experiments are conducted on various benchmarks. The superior performance over the state-of-the-art demonstrates the effectiveness of our method.

Performance

result
We achieved SOTA on many video anomaly detection datasets.

Unsupervised Anomaly Detection Model Training

bash train.sh

Unsupervised Anomaly Detection Model Testing

bash test.sh

If you find this work helpful, please cite:

@inproceedings{Nv2021APN,
  author    = {Chao Hu and
	       Fan Wu and
               Weijie Wu and
               Weibin Qiu and
               Shengxin Lai},
  title     = {Normal Learning in Videos with Attention Prototype Network},
  booktitle = {Computer Vision and Pattern Recognition},
  year      = {2021}
}
Multi-Output Gaussian Process Toolkit

Multi-Output Gaussian Process Toolkit Paper - API Documentation - Tutorials & Examples The Multi-Output Gaussian Process Toolkit is a Python toolkit f

GAMES 113 Nov 25, 2022
The AugNet Python module contains functions for the fast computation of image similarity.

AugNet AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation arxiv link In our work, we propose AugNet, a new deep le

Ming 74 Dec 28, 2022
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection arXi

59 Nov 29, 2022
PyTorch Implementation of Realtime Multi-Person Pose Estimation project.

PyTorch Realtime Multi-Person Pose Estimation This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here Realtime_Multi-P

Dave Fang 157 Nov 12, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks

P-tuning v2 P-Tuning v2: Prompt Tuning Can Be Comparable to Finetuning Universally Across Scales and Tasks An optimized prompt tuning strategy achievi

THUDM 540 Dec 30, 2022
True Few-Shot Learning with Language Models

This codebase supports using language models (LMs) for true few-shot learning: learning to perform a task using a limited number of examples from a single task distribution.

Ethan Perez 124 Jan 04, 2023
PyTorch implementation of UPFlow (unsupervised optical flow learning)

UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning By Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, Jian Sun Megvii

kunming luo 87 Dec 20, 2022
The 2nd place solution of 2021 google landmark retrieval on kaggle.

Leaderboard, taxonomy, and curated list of few-shot object detection papers.

229 Dec 13, 2022
Volsdf - Volume Rendering of Neural Implicit Surfaces

Volume Rendering of Neural Implicit Surfaces Project Page | Paper | Data This re

Lior Yariv 221 Jan 07, 2023
Breast Cancer Detection πŸ”¬ ITI "AI_Pro" Graduation Project

BreastCancerDetection - This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features

6 Nov 29, 2022
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021)

Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision (ICCV 2021) PyTorch implementation of Learning RAW-to-sRGB Mappings with Inaccurat

Zhilu Zhang 53 Dec 20, 2022
Improving Calibration for Long-Tailed Recognition (CVPR2021)

MiSLAS Improving Calibration for Long-Tailed Recognition Authors: Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia [arXiv] [slide] [BibTeX] Introductio

Jia Research Lab 116 Dec 20, 2022
Libraries, tools and tasks created and used at DeepMind Robotics.

Libraries, tools and tasks created and used at DeepMind Robotics.

DeepMind 270 Nov 30, 2022
Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients

LSF-SAC Pytorch implementations of the paper Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy G

Hanhan 2 Aug 14, 2022
Implementation of Change-Based Exploration Transfer (C-BET)

Implementation of Change-Based Exploration Transfer (C-BET), as presented in Interesting Object, Curious Agent: Learning Task-Agnostic Exploration.

Simone Parisi 29 Dec 04, 2022
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

Project This repo has been populated by an initial template to help get you started. Please make sure to update the content to build a great experienc

Microsoft 674 Dec 26, 2022
Python Algorithm Interview Book Review

파이썬 μ•Œκ³ λ¦¬μ¦˜ 인터뷰 μ±… 리뷰 리뷰 IT λŒ€κΈ°μ—…μ— λ“€μ–΄κ°€κ³  싢은 λͺ©ν‘œκ°€ μžˆλ‹€. λ‚΄κ°€ κΏˆκΏ”μ˜¨ νšŒμ‚¬μ—μ„œ μΌν•˜λŠ” μ‚¬λžŒλ“€μ˜ λͺ¨μŠ΅μ„ 보면 λ©‹μžˆλ‹€κ³  생각이 λ“€κ³  λ‚˜μ˜ λͺ©ν‘œμ— λŒ€ν•œ 열망이 κ°•ν•΄μ§€λŠ” 것 κ°™λ‹€. 미래의 핡심 사업 쀑 ν•˜λ‚˜μΈ SW 뢀뢄을 이끌고 λ°œμ „μ‹œν‚€λŠ” μš°λ¦¬λ‚˜λΌμ˜ I

SharkBSJ 1 Dec 14, 2021
The Easy-to-use Dialogue Response Selection Toolkit for Researchers

Easy-to-use toolkit for retrieval-based Chatbot Recent Activity Our released RRS corpus can be found here. Our released BERT-FP post-training checkpoi

GMFTBY 32 Nov 13, 2022
Code for "Optimizing risk-based breast cancer screening policies with reinforcement learning"

Tempo: Optimizing risk-based breast cancer screening policies with reinforcement learning Introduction This repository was used to develop Tempo, as d

Adam Yala 12 Oct 11, 2022