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This supplementary material presents detailed transformer model architectures, training parameters, and comprehensive evaluation metrics to complement our comparison of RNN and transformer models for Indonesian news classification. Our analysis provides deeper insights into why transformer models outperform RNN approaches despite their larger parameter counts.

Supp ro TASE 2025 submission

Supplemental Material

Supplement for the research paper: A PHYSICS-GUIDED SMOOTHING METHOD FOR MATERIAL MODELING WITH DIGITAL IMAGE CORRELATION (DIC) MEASUREMENTS

This document contains the supplementary material for the ICIP_2050 paper with ID #2494 and title "An End-to-End Class-Aware and Attention-Guided Model for Object State Classification".

Supplementary materials for paper Pose-free 3D Gaussian Splatting via Shape-Ray Estimation

Abstract

In the last few years, vision transformers have increasingly been adopted for medical image classification and other applications due to their improved accuracies compared to other deep learning models. However, due to their size and complex interactions via the self-attention mechanism, they are not well understood. In particular, it is unclear whether the representations produced by such models are semantically meaningful.

Fine-grained action localization in untrimmed sports videos is a challenging task, as motion transitions are subtle and occur within short time spans. Traditional supervised and weakly supervised methods require extensive labeled data, making them less scalable and generalizable. To address these challenges, we propose an unsupervised skeleton-based action localization pipeline that detects fine-grained action boundaries using spatio-temporal graph embeddings.

This paper presents COT-AD, a comprehensive Dataset designed to enhance cotton crop analysis through computer vision. Comprising over 25,000 images captured throughout the cotton growth cycle, with 5,000 annotated images, COT-AD includes aerial imagery for field-scale detection and segmentation and high-resolution DSLR images documenting key diseases. The annotations cover pest and disease recognition, vegetation, and weed analysis, addressing a critical gap in cotton-specific agricultural datasets.