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.
We welcome submissions on the following topics, including but not limited to:
|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 (email@example.com) and Prof. Ko (firstname.lastname@example.org)
|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|
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.Biography