Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Last update: Nov 20, 2022

Overview

Causality In Traffic Accident (Under Construction)

Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Overview

Data Preparation

Details of dataset construction

Benchmark

Cause and Effect Event Classification

We adopt Temporal Segment Networks (ECCV 2016) from the repository https://github.com/yjxiong/tsn-pytorch

The default arguments for code are set to train TSN with average consensus function.

python train_classifier.py --consensus_type average
python train_classifier.py --consensus_type linear

Temporal Cause and Effect Event Localization

We adopt three types of baseline methods in our benchmark.

Single-stage Action Detection

python train_localization.py --architecture_type forward-SST
python train_localization.py --architecture_type backward-SST
python train_localization.py --architecture_type bi-SST
python train_localization.py --architecture_type SSTCN-SST

Proposal-based Action Detection (Not supported yet)

python train_localization.py --architecture_type naive-conv-R-C3D
python train_localization.py --architecture_type SSTCN-R-C3D

Action Segmentation

python train_localization.py --architecture_type SSTCN-Segmentation
python train_localization.py --architecture_type MSTCN-Segmentation

Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Related tags

Overview

Causality In Traffic Accident (Under Construction)

Overview

Data Preparation

Benchmark

Cause and Effect Event Classification

Temporal Cause and Effect Event Localization

Owner

Tackgeun

Aspect-Sentiment-Multiple-Opinion Triplet Extraction (NLPCC 2021)

Direct Multi-view Multi-person 3D Human Pose Estimation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

House3D: A Rich and Realistic 3D Environment

CryptoFrog - My First Strategy for freqtrade

Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.

EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

A Convolutional Transformer for Keyword Spotting

Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Prototype-based Incremental Few-Shot Semantic Segmentation

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

Task-related Saliency Network For Few-shot learning

Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

KIDA: Knowledge Inheritance in Data Aggregation

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI

Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Related tags

Overview

Causality In Traffic Accident (Under Construction)

Overview

Data Preparation

Benchmark

Cause and Effect Event Classification

Temporal Cause and Effect Event Localization

Owner

Tackgeun

Aspect-Sentiment-Multiple-Opinion Triplet Extraction (NLPCC 2021)

Direct Multi-view Multi-person 3D Human Pose Estimation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

House3D: A Rich and Realistic 3D Environment

CryptoFrog - My First Strategy for freqtrade

Machine Learning Models were applied to predict the mass of the brain based on gender, age ranges, and head size.

EZ graph is an easy to use AI solution that allows you to make and train your neural networks without a single line of code.

PyTorch 1.5 implementation for paper DECOR-GAN: 3D Shape Detailization by Conditional Refinement.

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

A Convolutional Transformer for Keyword Spotting

Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

Prototype-based Incremental Few-Shot Semantic Segmentation

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

Task-related Saliency Network For Few-shot learning

Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

KIDA: Knowledge Inheritance in Data Aggregation

This is the pytorch implementation for the paper: *Learning Accurate Performance Predictors for Ultrafast Automated Model Compression*, which is in submission to TPAMI

Repository for RNNs using TensorFlow and Keras - LSTM and GRU Implementation from Scratch - Simple Classification and Regression Problem using RNNs

This is the pytorch implementation for the paper: Learning Accurate Performance Predictors for Ultrafast Automated Model Compression, which is in submission to TPAMI