Year: 2024
Type: conference-paper
Abstract: In this report, we describe the technical details of our submission to the WEAR Dataset Challenge 2024. For this competition, we use two approaches to boost the performance of the official WEAR GitHub repository. 1) Integration of a Temporal-Informative adapter (TIA) into the models of the WEAR repository; 2) Data Augmentation Techniques to enrich the provided test dataset. Our method achieves roughly 4.7% improved results on the test set of the WEAR Dataset Challenge 2024 compared to the baseline of the WEAR repository.
Authors: Maximilian Hopp, Helge Hartleb, Robin Burchard
Institution: University of Siegen
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Universität Siegen
OpenAlex ID: W4402721974