Abstract: Change detection is a critical task in earth observation applications. Recently, deep-learning-based methods have shown promising performance and are quickly adopted in change detection.
Abstract: Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical automatic identification system (AIS) data and accurately predict ...
Abstract: This article presents a new type of electromagnetic encoders based on step-impedance resonators (SIRs). The encoders consist of a linear chain of transversely oriented SIRs etched on a ...
Abstract: Visual encoders are fundamental components in vision-language models (VLMs), each showcasing unique strengths derived from various pre-trained visual foundation models. To leverage the ...
Abstract: Deep learning has significantly impacted the physical layer of wireless communications, leading to rapid advancements in this field. Auto-encoder, a self-supervised learning framework, has ...
This project will no longer be maintained by Intel. Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
We propose an encoder-decoder for open-vocabulary semantic segmentation comprising a hierarchical encoder-based cost map generation and a gradual fusion decoder. We introduce a category early ...
Abstract: The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks. In this paper, we present a lightweight network to address this problem, namely ...
Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its corresponding latent ...
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Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
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