The workshop will focus on various area of future impact of AI. The aim of this workshop is to bring researchers and scientists from academia, medical area with engineers from industry together to discuss about various impact of cutting-edge technologies of AI in future society. The workshop will take a deep dive into the capabilities of Edge Insights for Academia and Industrial via a tutorial utilizing real-world AI applications. For example, breast cancer can be detected via smartphone level infrared camera for detecting lesion and target mass at home. Despite the existence of some commercial AI systems such as autonomous vehicle, we are at the beginning of a long research pathway towards a future generation of deep AI. The workshop focuses on numerical and computational aspects of future impact of AI and on these relations to various AI techniques.

Call for papers

We welcome submissions on the following topics, including but not limited to:

  • Image Generation and Translation
  • Semantic segmentation/ Instance Segmentation
  • Recognition: detection, tracking, Anomaly detection, localization
  • Image processing: denoising, enhancement, super resolution
  • 3D computer vision, stereo matching
  • NLP (natural language processing)
  • Voice Recognition: STT(Speech to Text)
  • Reinforcement Learning
  • Sensor fusion with AI
  • Game with AI

  • Important Dates

    Paper Submission Deadline October 10, 2021 (23:59 Pacific time)
    Notification to Authors October 17, 2021
    Camera-Ready Deadline October 24, 2021
    Workshop Date November 12, 2021 (Morning)


    Extended Abstracts: Participants are encouraged to submit preliminary ideas that have not been previously published in conferences or journals. We also invite papers published in other conferences and journals (2021 only) to facilitate new collaborations. Submissions may consist of one page abstract and one additional page for references (using the template described above). The expanded abstract will be posted on the website only during the workshop period.

    All the papers should be submitted to workshop chairs, Prof. Lee ( and Prof. Ko (

    Workshop Schedule

    # Time Item
    1 9:30am - 10:00am Welcome and Opening Remarks
    2 10:00am - 10:40am Invited Speaker I
    3 10:40am - 11:10am Invited Speaker II
    4 11:10am - 11:50am Invited Speaker III
    5 11:50am - 12:00pm Panel Discussion
    6 13:00pm - 17:00pm Oral Presentation

    Invited Keynote Speakers

    Byoung Chul Ko
    Keimyung University

    Graph Convolution Neural Network Based Data association for Online Multi-Object Tracking


    A graph convolutional network (GCN)-based multi-object tracking (MOT) algorithm, consisting of a module for extracting the initial features and a module for updating the features, that estimates the affinity between nodes is proposed. The feature extraction module utilizes the pose feature of the object such that the tracking is correct even when the object is partially occluded. Unlike previous graph neural network (GNN)-based MOT methods, this study is based on a GCN and includes a new feature update mechanism, which is updated by combining the output of the neural network and the node similarity between the tracker and detection nodes for each layer. The node feature is updated by aggregating the updated edge feature and the connection strength between the tracker and detection. In each GCN layer, the three networks for the node, edge update, and edge classification were designed to minimize the network parameters to enable faster MOT compared to other GCN-based MOTs. The entire GCN network was designed to learn end-to-end through an affinity loss. The experimental results for the MOT16 and 17 challenge datasets show that the proposed method achieves a superior or similar performance in terms of tracking accuracy and speed compared to state-of-the-art methods, including GCN-based MOT.


    Accepted Papers

    Title Authors


    Jong-Ha Lee
    Keimyung University
    Byoung Chul Ko
    Keimyung University

    Program Committee

    Yo Han Park
    Keimyung University
    Deokwoo Lee
    Keimyung University
    Djamila Aouada
    Université du Luxembourg
    Hyung Jin Chang
    University of Birmingham
    Chang-Hee Won
    Temple University
    Shivendra Panwar
    New York University
    Youngjung Uh
    Yonsei University
    Changsu Lee
    Youngnam University
    SooYoung Kwak
    Hanbat University
    Inkyu Park
    Inha University


    This workshop is proudly sponsored by KMU(Keimyung University) Research Institute of AI fusion.


    For any related question, please contact Prof. Jong-Ha Lee (+82-10-8968-8769,