Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

Overview

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

This repository is for the following paper: "Investigating Attention Mechanism in 3D Point Cloud Object Detection"
Shi Qiu*, Yunfan Wu*, Saeed Anwar, Chongyi Li

Paper and citation

The paper can be downloaded from here (arXiv).
If you find our paper/codes/results are useful, please cite:

@article{qiu2021investigating,
  title={Investigating Attention Mechanism in 3D Point Cloud Object Detection},
  author={Qiu, Shi and Wu, Yunfan and Anwar, Saeed and Li, Chongyi},
  journal={arXiv preprint arXiv:2108.00620},
  year={2021}
}

Abstract

This project investigates the effects of five classical 2D attention modules (Non-local, Criss-cross, Squeeze-Excitation, CBAM, Dual-attention) and five novel 3D attention modules (Attentional-ShapeContextNet, Point-Attention, Channle Affinity Attention, Offset-Attention, Point-Transformer) in 3D point cloud object detection, based on VoteNet pipeline.

Our Attentional Backbone

Results

Visualization

Settings

Set up the VoteNet project, and replace the models/backbone_module.py file with ours.

Trained-models

The trained models can be downloaded from google drive.
The detailed evaluation logs reported in the paper can be found at google drive.

Acknowledgment

The code is built on VoteNet. We thank the authors for sharing the codes.

Owner
PhD student of ANU affiliated with Data61-CSIRO
Happywhale - Whale and Dolphin Identification Silver🥈 Solution (26/1588)

Kaggle-Happywhale Happywhale - Whale and Dolphin Identification Silver 🥈 Solution (26/1588) 竞赛方案思路 图像数据预处理-标志性特征图片裁剪:首先根据开源的标注数据训练YOLOv5x6目标检测模型,将训练集

Franxx 20 Nov 14, 2022
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD

Data Analysis Center 220 Dec 26, 2022
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation E2EC: An End-to-End Contour-based Method for High-Quality H

zhangtao 146 Dec 29, 2022
[CVPR 2021] Exemplar-Based Open-Set Panoptic Segmentation Network (EOPSN)

EOPSN: Exemplar-Based Open-Set Panoptic Segmentation Network (CVPR 2021) PyTorch implementation for EOPSN. We propose open-set panoptic segmentation t

Jaedong Hwang 49 Dec 30, 2022
PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

PyTorch code for EMNLP 2021 paper: Don't be Contradicted with Anything! CI-ToD: Towards Benchmarking Consistency for Task-oriented Dialogue System

Libo Qin 25 Sep 06, 2022
Evolution Strategies in PyTorch

Evolution Strategies This is a PyTorch implementation of Evolution Strategies. Requirements Python 3.5, PyTorch = 0.2.0, numpy, gym, universe, cv2 Wh

Andrew Gambardella 333 Nov 14, 2022
NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

NVIDIA Merlin NVIDIA Merlin is an open source library designed to accelerate recommender systems on NVIDIA’s GPUs. It enables data scientists, machine

419 Jan 03, 2023
Episodic-memory - Ego4D Episodic Memory Benchmark

Ego4D Episodic Memory Benchmark EGO4D is the world's largest egocentric (first p

3 Feb 18, 2022
The pytorch implementation of SOKD (BMVC2021).

Semi-Online Knowledge Distillation Implementations of SOKD. Requirements This repo was tested with Python 3.8, PyTorch 1.5.1, torchvision 0.6.1, CUDA

4 Dec 19, 2021
Deep Reinforcement Learning with pytorch & visdom

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

Jingwei Zhang 783 Jan 04, 2023
Image Restoration Using Swin Transformer for VapourSynth

SwinIR SwinIR function for VapourSynth, based on https://github.com/JingyunLiang/SwinIR. Dependencies NumPy PyTorch, preferably with CUDA. Note that t

Holy Wu 11 Jun 19, 2022
Exemplo de implementação do padrão circuit breaker em python

fast-circuit-breaker Circuit breakers existem para permitir que uma parte do seu sistema falhe sem destruir todo seu ecossistema de serviços. Michael

James G Silva 17 Nov 10, 2022
Kinetics-Data-Preprocessing

Kinetics-Data-Preprocessing Kinetics-400 and Kinetics-600 are common video recognition datasets used by popular video understanding projects like Slow

Kaihua Tang 7 Oct 27, 2022
The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

cv516Buaa 9 Nov 07, 2022
Hepsiburada - Hepsiburada Urun Bilgisi Cekme

Hepsiburada Urun Bilgisi Cekme from hepsiburada import Marka nike = Marka("nike"

Ilker Manap 8 Oct 26, 2022
Neural Scene Flow Fields using pytorch-lightning, with potential improvements

nsff_pl Neural Scene Flow Fields using pytorch-lightning. This repo reimplements the NSFF idea, but modifies several operations based on observation o

AI葵 178 Dec 21, 2022
Tgbox-bench - Simple TGBOX upload speed benchmark

TGBOX Benchmark This script will benchmark upload speed to TGBOX storage. Build

Non 1 Jan 09, 2022
The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"

TriageSQL The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text

Yusen Zhang 22 Nov 09, 2022
Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR

Image Restoration Toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSRGAN, SwinIR

Kai Zhang 2k Dec 31, 2022
Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

Evaluating the Factual Consistency of Abstractive Text Summarization Authors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher Int

Salesforce 165 Dec 21, 2022