Rotation invariance is essential for precise, object-level segmentation in UAV aerial imagery, where targets can have arbitrary orientations and exhibit fine-sc...
Industrial maintenance is being transformed by the Internet of Things and edge computing, generating continuous data streams that demand real-time, adaptive dec...
The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered ...
Vision-language models (VLMs) are emerging as powerful generalist tools for remote sensing, capable of integrating information across diverse tasks and enabling...
Real-world datasets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept ...
Large Language Models (LLMs) have recently demonstrated remarkable performance in generating high-quality tabular synthetic data. In practice, two primary appro...
Image captioning is essential in many fields including assisting visually impaired individuals, improving content management systems, and enhancing human-comput...
LLM agents are widely deployed in complex interactive tasks, yet privacy constraints often preclude centralized optimization and co-evolution across dynamic env...
The Development Knowledge Question Answering (Dev Knowledge QA) task aims to provide natural language answers to knowledge-seeking questions during software dev...
Gradually growing the depth of Transformers during training can not only reduce training cost but also lead to improved reasoning performance, as shown by MIDAS...
Understanding human personality is crucial for web applications such as personalized recommendation and mental health assessment. Existing studies on personalit...
As AI-based code generation becomes widespread, researchers are investigating the calibration of code LLMs - ensuring their confidence scores faithfully represe...
Despite advancements in machine learning for security, rule-based detection remains prevalent in Security Operations Centers due to the resource intensiveness a...
Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN ...
Document shadow removal is essential for enhancing the clarity of digitized documents. Preserving high-frequency details (e.g., text edges and lines) is critica...
This paper addresses the challenge of aligning large language models (LLMs) with diverse human preferences within federated learning (FL) environments, where st...
We propose a post-training method for lower-resource languages that preserves fluency of language models even when aligned by disfluent reward models. Preferenc...
In recent years, high-performance computer vision models have achieved remarkable success in medical imaging, with some skin lesion classification systems even ...
Automatic Sign Language Recognition (ASLR) has emerged as a vital field for bridging the gap between deaf and hearing communities. However, the problem of sign-...
Multigrid methods have been a popular approach for solving linear systems arising from the discretization of partial differential equations (PDEs) for several d...
In this paper, we investigate the potential of spatial and temporal cloud workload shifting to reduce carbon, water, and land-use footprints. Specifically, we p...
This paper introduces the first publicly available dataset for Automatic Essay Scoring (AES) and feedback generation in Basque, targeting the CEFR C1 proficienc...
With this paper, we introduce RESTifAI, an LLM-driven approach for generating reusable, CI/CD ready REST API tests, following the happy-path approach. Unlike ex...
Designing and implementing distributed systems correctly can be quite challenging. Although these systems are often accompanied by formal specifications that ar...
