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
UnpNet - Rethinking 3-D LiDAR Point Cloud Segmentation(IEEE TNNLS)

UnpNet Citation Please cite the following paper if you use this repository in your reseach. @article {PMID:34914599, Title = {Rethinking 3-D LiDAR Po

Shijie Li 4 Jul 15, 2022
This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

GBW This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices. Ap

Andi Han 0 Oct 22, 2021
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions (CVPR2021)

NExT-QA We reproduce some SOTA VideoQA methods to provide benchmark results for our NExT-QA dataset accepted to CVPR2021 (with 1 'Strong Accept' and 2

Junbin Xiao 50 Nov 24, 2022
The pytorch implementation of DG-Font: Deformable Generative Networks for Unsupervised Font Generation

DG-Font: Deformable Generative Networks for Unsupervised Font Generation The source code for 'DG-Font: Deformable Generative Networks for Unsupervised

130 Dec 05, 2022
[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

CPT: Efficient Deep Neural Network Training via Cyclic Precision Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin Accep

26 Oct 25, 2022
Sequence-to-Sequence learning using PyTorch

Seq2Seq in PyTorch This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train

Elad Hoffer 514 Nov 17, 2022
Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

Jieun Han 1 Oct 06, 2022
Multi-task Multi-agent Soft Actor Critic for SMAC

Multi-task Multi-agent Soft Actor Critic for SMAC Overview The CARE formulti-task: Multi-Task Reinforcement Learning with Context-based Representation

RuanJingqing 8 Sep 30, 2022
A Python type explainer!

typesplainer A Python typehint explainer! Available as a cli, as a website, as a vscode extension, as a vim extension Usage First, install the package

Typesplainer 79 Dec 01, 2022
Official pytorch implementation of Rainbow Memory (CVPR 2021)

Rainbow Memory: Continual Learning with a Memory of Diverse Samples

Clova AI Research 91 Dec 17, 2022
AAAI 2022 paper - Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

AT-BMC Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction (AAAI 2022) Paper Prerequisites Install pac

16 Nov 26, 2022
DANA paper supplementary materials

DANA Supplements This repository stores the data, results, and R scripts to generate these reuslts and figures for the corresponding paper Depth Norma

0 Dec 17, 2021
VOGUE: Try-On by StyleGAN Interpolation Optimization

VOGUE is a StyleGAN interpolation optimization algorithm for photo-realistic try-on. Top: shirt try-on automatically synthesized by our method in two different examples.

Wei ZHANG 66 Dec 09, 2022
Contrastive Learning Inverts the Data Generating Process

Official code to reproduce the results and data presented in the paper Contrastive Learning Inverts the Data Generating Process.

71 Nov 25, 2022
Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation The reference code of Improving Factual Completeness and C

46 Dec 15, 2022
labelpix is a graphical image labeling interface for drawing bounding boxes

Welcome to labelpix 👋 labelpix is a graphical image labeling interface for drawing bounding boxes. 🏠 Homepage Install pip install -r requirements.tx

schissmantics 26 May 24, 2022
[arXiv22] Disentangled Representation Learning for Text-Video Retrieval

Disentangled Representation Learning for Text-Video Retrieval This is a PyTorch implementation of the paper Disentangled Representation Learning for T

Qiang Wang 49 Dec 18, 2022
Geometric Algebra package for JAX

JAXGA - JAX Geometric Algebra GitHub | Docs JAXGA is a Geometric Algebra package on top of JAX. It can handle high dimensional algebras by storing onl

Robin Kahlow 36 Dec 22, 2022
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Fuhang 5 Jan 18, 2022
Reverse engineering Rosetta 2 in M1 Mac

Project Champollion About this project Rosetta 2 is an emulation mechanism to run the x86_64 applications on Arm-based Apple Silicon with Ahead-Of-Tim

FFRI Security, Inc. 258 Jan 07, 2023