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.

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Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Related tags

Overview

Physical Anomalous Trajectory or Motion (PHANTOM) Dataset

Description

Citation

Owner

Python Actor concurrency library

A python module for scientific analysis of 3D objects based on VTK and Numpy

Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

Collection of generative models in Tensorflow

Real-time object detection on Android using the YOLO network with TensorFlow

This is RFA-Toolbox, a simple and easy-to-use library that allows you to optimize your neural network architectures using receptive field analysis (RFA) and create graph visualizations of your architecture.

Inteligência artificial criada para realizar interação social com idosos.

Adversarial Learning for Modeling Human Motion

Code for our paper "Sematic Representation for Dialogue Modeling" in ACL2021

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Resources related to EMNLP 2021 paper "FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations"

Experiments on continual learning from a stream of pretrained models.

ACV is a python library that provides explanations for any machine learning model or data.

The software associated with a paper accepted at EMNLP 2021 titled "Open Knowledge Graphs Canonicalization using Variational Autoencoders".

In this project we predict the forest cover type using the cartographic variables in the training/test datasets.

Prior-Guided Multi-View 3D Head Reconstruction

This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.

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Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders

Half Instance Normalization Network for Image Restoration