Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Last update: Dec 16, 2021

Overview

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Description

This dataset contains the six different classes as described in our paper[]. These classes are designed to be used to detect anomalies in physical videos and are meant to be used as a benchmark to assess a method's performance on physical videos. The train set contains normal videos, the test set contains both normal and anomalous videos.

In addition, we provide a script to obtain the test and train video id's of the Something-Something V2 dataset. This too is meant to serve as even more difficult benchmark for general video anomaly detection. Our approach is decribed in the paper.

Citation

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GitHub Repository

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Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

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Overview

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Description

Citation

Owner

[CVPR 21] Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2021.

PyTorch implementation of our ICCV 2019 paper: Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis

Code for the TPAMI paper: "Syntax Customized Video Captioning by Imitating Exemplar Sentences"

A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation

Code repo for "Transformer on a Diet" paper

PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

CondenseNet: Light weighted CNN for mobile devices

Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision

Exact Pareto Optimal solutions for preference based Multi-Objective Optimization

The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

Learning Dense Representations of Phrases at Scale (Lee et al., 2020)

🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

Implementation of UNet on the Joey ML framework

StellarGraph - Machine Learning on Graphs

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

Out-of-Town Recommendation with Travel Intention Modeling (AAAI2021)

Pytorch implemenation of Stochastic Multi-Label Image-to-image Translation (SMIT)

Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Toontown House CT Edition

Explanatory Learning: Beyond Empiricism in Neural Networks