Publication Type
Conference Paper
Version
acceptedVersion
Publication Date
8-2024
Abstract
In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
IJCAI 2024 Explainable Artificial Intelligence (XAI) Workshop, Virtual Conference, August 15
Identifier
10.48550/arXiv.2405.17444
Publisher
Springer
City or Country
Virtual Conference
Citation
LEE, Min Hun.
Towards gradient-based time-series explanations through a spatiotemporal attention network. (2024). IJCAI 2024 Explainable Artificial Intelligence (XAI) Workshop, Virtual Conference, August 15.
Available at: https://ink.library.smu.edu.sg/sis_research/9959
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
https://sites.google.com/view/xai2024/proceedings?authuser=0 https://arxiv.org/abs/2405.17444