This is the official pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering" on VQA Task

Related tags

Deep LearningERASOR
Overview

🌈 ERASOR (RA-L'21 with ICRA Option)

Official page of "ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building", which is accepted by RA-L with ICRA'21 option [Demo Video].

overview

We provide all contents including

  • Source code of ERASOR
  • All outputs of the State-of-the-arts
  • Visualization
  • Calculation code of Preservation Rate/Rejection Rate

So enjoy our codes! :)

Contact: Hyungtae Lim ([email protected])

Advisor: Hyun Myung ([email protected])

Contents

  1. Test Env.
  2. Requirements
  3. How to Run ERASOR
  4. Calculate PR/RR
  5. Benchmark
  6. Run Your Own Code
  7. Visualization of All the State-of-the-arts
  8. Citation

Test Env.

The code is tested successfully at

  • Linux 18.04 LTS
  • ROS Melodic

Requirements

ROS Setting

  • Install ROS on a machine.
  • Also, jsk-visualization is required to visualize Scan Ratio Test (SRT) status.
sudo apt-get install ros-melodic-jsk-recognition
sudo apt-get install ros-melodic-jsk-common-msgs
sudo apt-get install ros-melodic-jsk-rviz-plugins

Buildg Our Package

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone https://github.com/LimHyungTae/ERASOR.Official.git
cd .. && catkin build erasor 

Python Setting

  • Our metric calculation for PR/RR code is implemented by python2.7
  • To run the python code, following pakages are necessary: pypcd, tqdm, scikit-learn, and tabulate
pip install pypcd
pip install tqdm	
pip install scikit-learn
pip install tabulate

Prepared dataset

  • Download the preprocessed KITTI data encoded into rosbag.
  • The downloading process might take five minutes or so. All rosbags requires total 2.3G of storage space
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/00_4390_to_4530_w_interval_2_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/01_150_to_250_w_interval_1_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/02_860_to_950_w_interval_2_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/05_2350_to_2670_w_interval_2_node.bag
wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/07_630_to_820_w_interval_2_node.bag

Description of Preprocessed Rosbag Files

  • Please note that the rosbag consists of node. Refer to msg/node.msg.
  • Note that each label of the point is assigned in intensity for the sake of convenience.
  • And we set the following classes are dynamic classes:
# 252: "moving-car"
# 253: "moving-bicyclist"
# 254: "moving-person"
# 255: "moving-motorcyclist"
# 256: "moving-on-rails"
# 257: "moving-bus"
# 258: "moving-truck"
# 259: "moving-other-vehicle"
  • Please refer to std::vector DYNAMIC_CLASSES in our code :).

How to Run ERASOR

We will explain how to run our code on seq 05 of the KITTI dataset as an example.

Step 1. Build naive map

kittimapgen

  • Set the following parameters in launch/mapgen.launch.
    • target_rosbag: The name of target rosbag, e.g. 05_2350_to_2670_w_interval_2_node.bag
    • save_path: The path where the naively accumulated map is saved.
  • Launch mapgen.launch and play corresponding rosbag on the other bash as follows:
roscore # (Optional)
roslaunch erasor mapgen.launch
rosbag play 05_2350_to_2670_w_interval_2_node.bag
  • Then, dense map and voxelized map are auto-saved at the save path. Note that the dense map is used to fill corresponding labels (HERE). The voxelized map will be an input of step 2 as a naively accumulated map.

Step 2. Run ERASOR erasor

  • Set the following parameters in config/seq_05.yaml.

    • initial_map_path: The path of naively accumulated map
    • save_path: The path where the filtered static map is saved.
  • Run the following command for each bash.

roscore # (Optional)
roslaunch erasor run_erasor.launch target_seq:="05"
rosbag play 05_2350_to_2672_w_interval_2_node.bag
  • IMPORTANT: After finishing running ERASOR, run the following command to save the static map as a pcd file on another bash.
  • "0.2" denotes voxelization size.
rostopic pub /saveflag std_msgs/Float32 "data: 0.2"
  • Then, you can see the printed command as follows:

fig_command

  • The results will be saved under the save_path folder, i.e. $save_path$/05_result.pcd.

Calculate PR/RR

You can check our results directly.

  • First, download all pcd materials.
wget https://urserver.kaist.ac.kr/publicdata/erasor/erasor_paper_pcds.zip
unzip erasor_paper_pcds.zip

Then, run the analysis code as follows:

python analysis.py --gt $GT_PCD_PATH$ --est $EST_PCD_PATH$

E.g,

python analysis.py --gt /home/shapelim/erasor_paper_pcds/gt/05_voxel_0_2.pcd --est /home/shapelim/erasor_paper_pcds/estimate/05_ERASOR.pcd

NOTE: For estimating PR/RR, more dense pcd file, which is generated in the mapgen.launch procedure, is better to estimate PR/RR precisely.

Benchmark

  • Error metrics are a little bit different from those in the paper:

    Seq. PR [%] RR [%]
    00 91.72 97.00
    01 91.93 94.63
    02 81.08 99.11
    05 86.98 97.88
    07 92.00 98.33
  • But we provide all pcd files! Don't worry. See Visualization of All the State-of-the-arts Section.

Run Your Own Code

⚠️ TBU: The code is already in this repository, yet the explanation is incomplete.

Visualization of All the State-of-the-arts

  • First, download all pcd materials.
wget https://urserver.kaist.ac.kr/publicdata/erasor/erasor_paper_pcds.zip
unzip erasor_paper_pcds.zip
  • Set parameters in config/viz_params.yaml correctly

    • abs_dir: The absolute directory of pcd directory
    • seq: Target sequence (00, 01, 02, 05, or 07)
  • After setting the parameters, launch following command:

roslaunch erasor compare_results.launch

Citation

If you use our code or method in your work, please consider citing the following:

@article{lim2021erasor,
title={ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building},
author={Lim, Hyungtae and Hwang, Sungwon and Myung, Hyun},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={2},
pages={2272--2279},
year={2021},
publisher={IEEE}
}
Owner
Hyungtae Lim
Ph.D Candidate of URL lab. @ KAIST, South Korea
Hyungtae Lim
StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN

StyleGAN of All Trades: Image Manipulation withOnly Pretrained StyleGAN This is the PyTorch implementation of StyleGAN of All Trades: Image Manipulati

360 Dec 28, 2022
The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp.

PISE The code for our CVPR paper PISE: Person Image Synthesis and Editing with Decoupled GAN, Project Page, supp. Requirement conda create -n pise pyt

jinszhang 110 Nov 21, 2022
This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

LiDARTag Overview This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds (PDF)(arXiv). This wo

University of Michigan Dynamic Legged Locomotion Robotics Lab 159 Dec 21, 2022
Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Retrieval.

Targeted Trojan-Horse Attacks on Language-based Image Retrieval Source code of our TTH paper: Targeted Trojan-Horse Attacks on Language-based Image Re

fine 7 Aug 23, 2022
Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. Not an official Google product. Me

Google Research 27 Dec 12, 2022
code for "Feature Importance-aware Transferable Adversarial Attacks"

Feature Importance-aware Attack(FIA) This repository contains the code for the paper: Feature Importance-aware Transferable Adversarial Attacks (ICCV

Hengchang Guo 44 Nov 24, 2022
Hierarchical Memory Matching Network for Video Object Segmentation (ICCV 2021)

Hierarchical Memory Matching Network for Video Object Segmentation Hongje Seong, Seoung Wug Oh, Joon-Young Lee, Seongwon Lee, Suhyeon Lee, Euntai Kim

Hongje Seong 72 Dec 14, 2022
(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

Realistic evaluation of transductive few-shot learning Introduction This repo contains the code for our NeurIPS 2021 submitted paper "Realistic evalua

Olivier Veilleux 14 Dec 13, 2022
S-attack library. Official implementation of two papers "Are socially-aware trajectory prediction models really socially-aware?" and "Vehicle trajectory prediction works, but not everywhere".

S-attack library: A library for evaluating trajectory prediction models This library contains two research projects to assess the trajectory predictio

VITA lab at EPFL 71 Jan 04, 2023
T2F: text to face generation using Deep Learning

⭐ [NEW] ⭐ T2F - 2.0 Teaser (coming soon ...) Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN

Animesh Karnewar 533 Dec 22, 2022
Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks by Ángel López García-Arias, Masanori Hashimoto, Masato Motomura, and J

Ángel López García-Arias 4 May 19, 2022
PyTorch implementation of Wide Residual Networks with 1-bit weights by McDonnell (ICLR 2018)

1-bit Wide ResNet PyTorch implementation of training 1-bit Wide ResNets from this paper: Training wide residual networks for deployment using a single

Sergey Zagoruyko 122 Dec 07, 2022
"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback"

This is code repo for our EMNLP 2017 paper "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback", which implements the A2C algorithm on top of a neural encoder-

Khanh Nguyen 131 Oct 21, 2022
Teaching end to end workflow of deep learning

Deep-Education This repository is now available for public use for teaching end to end workflow of deep learning. This implies that learners/researche

Data Lab at College of William and Mary 2 Sep 26, 2022
Replication attempt for the Protein Folding Model

RGN2-Replica (WIP) To eventually become an unofficial working Pytorch implementation of RGN2, an state of the art model for MSA-less Protein Folding f

Eric Alcaide 36 Nov 29, 2022
Evaluating saliency methods on artificial data with different background types

Evaluating saliency methods on artificial data with different background types This repository contains the relevant code for the MedNeurips 2021 subm

2 Jul 05, 2022
The official project of SimSwap (ACM MM 2020)

SimSwap: An Efficient Framework For High Fidelity Face Swapping Proceedings of the 28th ACM International Conference on Multimedia The official reposi

Six_God 2.6k Jan 08, 2023
Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)

Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N

Jaeho Lee 41 Nov 10, 2022
The codes and models in 'Gaze Estimation using Transformer'.

GazeTR We provide the code of GazeTR-Hybrid in "Gaze Estimation using Transformer". We recommend you to use data processing codes provided in GazeHub.

65 Dec 27, 2022
deep learning for image processing including classification and object-detection etc.

深度学习在图像处理中的应用教程 前言 本教程是对本人研究生期间的研究内容进行整理总结,总结的同时也希望能够帮助更多的小伙伴。后期如果有学习到新的知识也会与大家一起分享。 本教程会以视频的方式进行分享,教学流程如下: 1)介绍网络的结构与创新点 2)使用Pytorch进行网络的搭建与训练 3)使用Te

WuZhe 13.6k Jan 04, 2023