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arXiv:2501.10454v1 Announce Type: new Abstract: Spatio-Temporal graph convolutional networks were originally introduced with CNNs as temporal blocks for feature extraction. Since then LSTM temporal blocks have been proposed and shown to have promising results. We propose a novel architecture combining both CNN and LSTM temporal blocks and then provide an empirical comparison between our new and the pre-existing models. We provide theoretical arguments for the different temporal blocks and use a multitude of tests across different datasets to assess our hypotheses. Source link lol

arXiv:2501.10615v1 Announce Type: new Abstract: Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segmentation, they remain challenging due to the intricate multiscale structure and the complexity of the surrounding tissues. This paper presents a novel approach for enhancing aorta segmentation using a Bayesian neural network-based hierarchical Laplacian of Gaussian (LoG) model. Our model consists of a 3D U-Net stream and a hierarchical LoG stream: the former provides an initial aorta segmentation, and the latter enhances blood vessel detection across varying scales by learning suitable…

[Submitted on 18 Jan 2025] View a PDF of the paper titled Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations, by Raha Aghaei and 6 other authors View PDF Abstract:Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the current applications, future directions, and strategic recommendations for marketers. In particular, we focus on LLMs major business drivers such as personalization, real-time-interactive customer insights, and content automation, and how they…

[Submitted on 14 Jan 2025] Authors:Shuzhou Sun (1 and 2), Li Liu (3), Yongxiang Liu (3), Zhen Liu (3), Shuanghui Zhang (3), Janne Heikkilä (2), Xiang Li (3) ((1) The College of Computer Science, Nankai University, Tianjin, China, (2) The Center for Machine Vision and Signal Analysis, University of Oulu, Finland, (3) The College of Electronic Science, National University of Defense Technology, China) View a PDF of the paper titled Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation, by Shuzhou Sun (1 and 2) and 15 other authors View PDF HTML (experimental) Abstract:Bias in Foundation Models (FMs) -…

arXiv:2501.10604v1 Announce Type: new Abstract: The increasing availability of traffic videos functioning on a 24/7/365 time scale has the great potential of increasing the spatio-temporal coverage of traffic accidents, which will help improve traffic safety. However, analyzing footage from hundreds, if not thousands, of traffic cameras in a 24/7/365 working protocol remains an extremely challenging task, as current vision-based approaches primarily focus on extracting raw information, such as vehicle trajectories or individual object detection, but require laborious post-processing to derive actionable insights. We propose SeeUnsafe, a new framework that integrates Multimodal Large Language Model (MLLM) agents to transform video-based traffic…