Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022)

Related tags

Deep LearningUDAT
Overview

Unsupervised Domain Adaptation for Nighttime Aerial Tracking (CVPR2022)

Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, and Guang Chen. Unsupervised Domain Adaptation for Nighttime Aerial Tracking. In CVPR, pages 1-10, 2022.

featured

Overview

UDAT is an unsupervised domain adaptation framework for visual object tracking. This repo contains its Python implementation.

Paper | NAT2021 benchmark

Testing UDAT

1. Preprocessing

Before training, we need to preprocess the unlabelled training data to generate training pairs.

  1. Download the proposed NAT2021-train set

  2. Customize the directory of the train set in lowlight_enhancement.py and enhance the nighttime sequences

    cd preprocessing/
    python lowlight_enhancement.py # enhanced sequences will be saved at '/YOUR/PATH/NAT2021/train/data_seq_enhanced/'
  3. Download the video saliency detection model here and place it at preprocessing/models/checkpoints/.

  4. Predict salient objects and obtain candidate boxes

    python inference.py # candidate boxes will be saved at 'coarse_boxes/' as .npy
  5. Generate pseudo annotations from candidate boxes using dynamic programming

    python gen_seq_bboxes.py # pseudo box sequences will be saved at 'pseudo_anno/'
  6. Generate cropped training patches and a JSON file for training

    python par_crop.py
    python gen_json.py

2. Train

Take UDAT-CAR for instance.

  1. Apart from above target domain dataset NAT2021, you need to download and prepare source domain datasets VID and GOT-10K.

  2. Download the pre-trained daytime model (SiamCAR/SiamBAN) and place it at UDAT/tools/snapshot.

  3. Start training

    cd UDAT/CAR
    export PYTHONPATH=$PWD
    python tools/train.py

3. Test

Take UDAT-CAR for instance.

  1. For quick test, you can download our trained model for UDAT-CAR (or UDAT-BAN) and place it at UDAT/CAR/experiments/udatcar_r50_l234.

  2. Start testing

    python tools/test.py --dataset NAT

4. Eval

  1. Start evaluating
    python tools/eval.py --dataset NAT

Demo

Demo video

Reference

@Inproceedings{Ye2022CVPR,

title={{Unsupervised Domain Adaptation for Nighttime Aerial Tracking}},

author={Ye, Junjie and Fu, Changhong and Zheng, Guangze and Paudel, Danda Pani and Chen, Guang},

booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

year={2022},

pages={1-10}

}

Acknowledgments

We sincerely thank the contribution of following repos: SiamCAR, SiamBAN, DCFNet, DCE, and USOT.

Contact

If you have any questions, please contact Junjie Ye at [email protected] or Changhong Fu at [email protected].

Owner
Intelligent Vision for Robotics in Complex Environment
Adaptive Vision for Robotics in Complex Environment
Intelligent Vision for Robotics in Complex Environment
Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

DataSelection-NMT Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts Quick update: The paper got accepted o

Javad Pourmostafa 6 Jan 07, 2023
The official PyTorch code implementation of "Personalized Trajectory Prediction via Distribution Discrimination" in ICCV 2021.

Personalized Trajectory Prediction via Distribution Discrimination (DisDis) The official PyTorch code implementation of "Personalized Trajectory Predi

25 Dec 20, 2022
Sdf sparse conv - Deep Learning on SDF for Classifying Brain Biomarkers

Deep Learning on SDF for Classifying Brain Biomarkers To reproduce the results f

1 Jan 25, 2022
Official code release for 3DV 2021 paper Human Performance Capture from Monocular Video in the Wild.

Official code release for 3DV 2021 paper Human Performance Capture from Monocular Video in the Wild.

Chen Guo 58 Dec 24, 2022
Practical and Real-world applications of ML based on the homework of Hung-yi Lee Machine Learning Course 2021

Machine Learning Theory and Application Overview This repository is inspired by the Hung-yi Lee Machine Learning Course 2021. In that course, professo

SilenceJiang 35 Nov 22, 2022
TLDR: Twin Learning for Dimensionality Reduction

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self

NAVER 105 Dec 28, 2022
Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

M-LSD: Towards Light-weight and Real-time Line Segment Detection Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Det

123 Jan 04, 2023
Details about the wide minima density hypothesis and metrics to compute width of a minima

wide-minima-density-hypothesis Details about the wide minima density hypothesis and metrics to compute width of a minima This repo presents the wide m

Nikhil Iyer 9 Dec 27, 2022
The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter

FAPIS The official implementation of the CVPR 2021 paper FAPIS: a Few-shot Anchor-free Part-based Instance Segmenter Introduction This repo is primari

Khoi Nguyen 8 Dec 11, 2022
Adjust Decision Boundary for Class Imbalanced Learning

Adjusting Decision Boundary for Class Imbalanced Learning This repository is the official PyTorch implementation of WVN-RS, introduced in Adjusting De

Peyton Byungju Kim 16 Jan 04, 2023
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's algorithm.

Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil

AaltoML 165 Nov 27, 2022
Speeding-Up Back-Propagation in DNN: Approximate Outer Product with Memory

Approximate Outer Product Gradient Descent with Memory Code for the numerical experiment of the paper Speeding-Up Back-Propagation in DNN: Approximate

2 Mar 02, 2022
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

FPGA & FreeNet Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification by Zhuo Zheng, Yanfei Zhong, Ailong M

Zhuo Zheng 92 Jan 03, 2023
PyTorch source code for Distilling Knowledge by Mimicking Features

LSHFM.detection This is the PyTorch source code for Distilling Knowledge by Mimicking Features. And this project contains code for object detection wi

Guo-Hua Wang 4 Dec 17, 2022
Implementation of Fast Transformer in Pytorch

Fast Transformer - Pytorch Implementation of Fast Transformer in Pytorch. This only work as an encoder. Yannic video AI Epiphany Install $ pip install

Phil Wang 167 Dec 27, 2022
Official repository for MixFaceNets: Extremely Efficient Face Recognition Networks

MixFaceNets This is the official repository of the paper: MixFaceNets: Extremely Efficient Face Recognition Networks. (Accepted in IJCB2021) https://i

Fadi Boutros 51 Dec 13, 2022
An Approach to Explore Logistic Regression Models

User-centered Regression An Approach to Explore Logistic Regression Models This tool applies the potential of Attribute-RadViz in identifying correlat

0 Nov 12, 2021
Some pre-commit hooks for OpenMMLab projects

pre-commit-hooks Some pre-commit hooks for OpenMMLab projects. Using pre-commit-hooks with pre-commit Add this to your .pre-commit-config.yaml - rep

OpenMMLab 16 Nov 29, 2022
Easily pull telemetry data and create beautiful visualizations for analysis.

This repository is a work in progress. Anything and everything is subject to change. Porpo Table of Contents Porpo Table of Contents General Informati

Ryan Dawes 33 Nov 30, 2022