UAVM 2023

ACM Multimedia

ACM MM 2023 (https://www.acmmm2023.org/)

Workshop on

UAVs in Multimedia: Capturing the World from a New Perspective (UAVM 2023)

The accept papers will be published at ACM Multimedia Workshop (top 50%), and go through the same peer review process as the regular papers. Several authors will be invited to do a oral presentation.

[Accepted Workshop Proposal] [Submission Site]

Join our Google Group for important updates.

News

  • 15/7/2023 - Challenge Open-source Code.
  • 23/4/2023 - Challenge Platform is now available.
  • 7/4/2023 - Paper submission site is now available.
  • 6/4/2023 - CFP is released.
  • 6/4/2023 - Workshop homepage is now available.

Challenge Open-source Codes

Please check https://github.com/layumi/UAVM2023

Important Dates

Submission of papers:

  • Workshop Papers Submission: 5 July 2023 13 July 2023
  • Workshop Papers Notification: 30 July 2023
  • Student Travel Grants Application Deadline: 5 August 2023
  • Camera-ready Submission: 6 August 2023
  • Conference Dates: 28 October 2023 – 3 November 2023

Please note: The submission deadline is at 11:59 p.m. of the stated deadline date Anywhere on Earth

Abstract

Unmanned Aerial Vehicles (UAVs), also known as drones, have become increasingly popular in recent years due to their ability to capture high-quality multimedia data from the sky. With the rise of multimedia applications, such as aerial photography, cinematography, and mapping, UAVs have emerged as a powerful tool for gathering rich and diverse multimedia content. This workshop aims to bring together researchers, practitioners, and enthusiasts interested in UAV multimedia to explore the latest advancements, challenges, and opportunities in this exciting field. The workshop will cover various topics related to UAV multimedia, including aerial image and video processing, machine learning for UAV data analysis, UAV swarm technology, and UAV-based multimedia applications. In the context of the ACM Multimedia conference, this workshop is highly relevant as multimedia data from UAVs is becoming an increasingly important source of content for many multimedia applications. The workshop will provide a platform for researchers to share their work and discuss potential collaborations, as well as an opportunity for practitioners to learn about the latest developments in UAV multimedia technology. Overall, this workshop will provide a unique opportunity to explore the exciting and rapidly evolving field of UAV multimedia and its potential impact on the wider multimedia community.

The list of possible topics includes, but is not limited to:

  • Video-based UAV Navigation
    • Satellite-guided & Ground-guided Navigation
    • Path Planning and Obstacle Avoidance
    • Visual SLAM (Simultaneous Localization and Mapping)
    • Sensor Fusion and Reinforcement Learning for Navigation
  • UAV Swarm Coordination
    • Multiple Platform Collaboration
    • Multi-agent Cooperation and Communication
    • Decentralized Control and Optimization
    • Distributed Perception and Mapping
  • UAV-based Object Detection and Tracking
    • Aerial-view Object Detection, Tracking and Re-identification
    • Aerial-view Action Recognition
  • UAV-based Sensing and Mapping
    • 3D Mapping and Reconstruction
    • Remote Sensing and Image Analysis
    • Disaster Response and Relief
  • UAV-based Delivery and Transportation
    • Package Delivery and Logistics
    • Safety and Regulations for UAV-based Transportation

Submission Types

Paper can be submitted on [Open Review].

Submission template can be found at ACM or you may directly follow the overleaf template.

In this workshop, we welcome four types of submissions, all of which should relate to the topics and themes as listed in Section 3:

  • (1). Position or perspective papers (up to 4 pages in length, plus unlimited pages for references): original ideas, perspectives, research vision, and open challenges in the area of evaluation approaches for explainable recommender systems;
  • (2). Challenge papers (up to 4 pages in length, plus unlimited pages for references): original solution to the Challenge data, University160k, in terms of effectiveness and efficiency.
  • (3). Featured papers (title and abstract of the paper, plus the original paper): already published papers or papers summarizing existing publications in leading conferences and highimpact journals that are relevant for the topic of the workshop;
  • (4). Demonstration papers (up to 2 pages in length, plus unlimited pages for references): original or already published prototypes and operational evaluation approaches in the area of explainable recommender systems. Page limits include diagrams and appendices. Submissions should be single-blind, written in English, and formatted according to the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use “sigconf” proceedings template for LaTeX and the Interim Template for Word).

Tips:

  • For privacy protection, please blur faces in the published materials (such as paper, video, poster, etc.)
  • For social good, please do not contain any misleading words, such as surveillance and secret.

Challenge

Challenge Platform is at https://codalab.lisn.upsaclay.fr/competitions/12672 .

We also provide a challenging cross-view geo-localization dataset, called University160k, and the workshop audience may consider to participate the competition. The motivation is to simulate the real- world geo-localization scenario that we usually face an extremely large satellite-view pool. In particular, University160k extends the current University-1652 dataset with extra 167,486 satellite- view gallery distractors. We will release University160k on our website, and make a public leader board. These distractor satellite- view images have a size of 1024 × 1024 and are obtained by cutting orthophoto images of real urban and surrounding areas. The larger image size ensures higher image clarity, while the wider framing range allows the images to contain more diverse scenes, such as buildings, city roads, trees, fields, and more (see Figure 3). In our primary evaluation, the distractor is challenging and make the competitive baseline model, LPN, decrease the Recall@1 accuracy from 75.93% to 64.85% and the value of AP from 79.14% to 67.69% in the Drone → Satellite task (Please see Table 2). We hope more audiences can be involved to solve this challenge, and may also consider the efficiency problem against a large candidate pool.

Check challenge details at Section 5 in https://zdzheng.xyz/files/ACMMM23_Workshop_Drone.pdf

The challenge dataset contains two part.

  1. The basic dataset (training set) can be download by Request. Usually I will reply the download link in 5 minutes.

  2. The name-masked test-160k dataset (query & gallery+distractor) can be downloaded from Onedrive.

(In the future, you also can download the name-unmasked distractor dataset to quiclyt report number in your paper (Please add to satellite gallery) can be downloaded from Onedrive, Google Drive, or Baidu Disk(https://pan.baidu.com/s/15TDqJIkEVv2r1fWlLQFLPw Code:78xf).)

The submission example can be found at Baseline Submission. Please zip it as ``answer.zip’’ to submit the result.

Please return the top-10 satellite names. For example, the first query is Y2HVQvCQIwVmwzq.jpeg''. Therefore, the first line of returned result inanswer.txt’’ should be the format as follows:

LJMJGM5vTQM3iRy	ValP4k9neTZffLz	Co1CEWkBhHdTAM2	w2Nk6LrN5p2cF54	FuMp6XdwlRqScG2	4WVhVPBkr8TJTNJ	y7XiwY8lWpMZNar	AQZgRYUIyvpUnz8	bziEPp56rwI7e7E	qI9WAxrCnbaqjIq

Please return the result following the order of query at Query TXT It will be 37855 lines.

Organizing Team
Zhedong Zheng, National University of Singapore, Singapore Yujiao Shi, Australian National University, Australia Tingyu Wang, Hangzhou Dianzi University, China
Jun Liu, Singapore University of Technology and Design, Singapore Jianwu Fang, Chang’an University, China Yunchao Wei, Beijing Jiaotong University, China
   
Tat-Seng Chua, National University of Singapore, Singapore    

Conference and Journal Papers

All papers presented at ACMMM 2023 will be included in ACM proceeding. All papers submitted to this workshop will go through the same review process as the regular papers submitted to the main conference to ensure that the contributions are of high quality.

Student Traval Funding

Please check https://www.acmmm2023.org/student-travel-grants/

Application Deadline: August 5, 2023

Workshop Citation

@inproceedings{zheng2023UVA,
  title={UAVM '23: 2023 Workshop on UAVs in Multimedia: Capturing the World from a New Perspective},
  author={Zheng, Zhedong and Shi, Yujiao and Wang, Tingyu and Liu, Jun and Fang, Jianwu and Wei, Yunchao and Chua, Tat-seng},
  booktitle={Proceedings of the 31th ACM International Conference on Multimedia Workshop},
  year={2023}
}