相关论文: CKMImageNet: A Comprehensive Dataset to Enable Cha…

Accurate location information is indispensable for the emerging applications of \ac{iov}, such as automatic driving and formation control. In the real scenario, vision-based localization has demonstrated superior performance to other…

A novel location-aware beamforming scheme for millimeter wave communication is proposed for line of sight (LOS) and low mobility scenarios, in which computer vision is introduced to derive the required position or spatial angular…

We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner…

Humans construct internal cognitive maps of their environment directly from sensory inputs without access to a system of explicit coordinates or distance measurements. While machine learning algorithms like SLAM utilize specialized visual…

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial…

Mobile robots in unknown cluttered environments with irregularly shaped obstacles often face energy and communication challenges which directly affect their ability to explore these environments. In this paper, we introduce a novel deep…

Learning representations through self-supervision on unlabeled data has proven highly effective for understanding diverse images. However, remote sensing images often have complex and densely populated scenes with multiple land objects and…

Making a single network effectively address diverse contexts---learning the variations within a dataset or multiple datasets---is an intriguing step towards achieving generalized intelligence. Existing approaches of deepening, widening, and…

Indoor navigation aims at performing navigation within buildings. In scenes like home and factory, most intelligent mobile devices require an functionality of routing to guide itself precisely through indoor scenes to complete various tasks…

With the accumulation of big data of CME observations by coronagraphs, automatic detection and tracking of CMEs has proven to be crucial. The excellent performance of convolutional neural network in image classification, object detection…

In recent years, significant advancements have been made in deep learning for medical image segmentation, particularly with convolutional neural networks (CNNs) and transformer models. However, CNNs face limitations in capturing long-range…

In order to withstand the ever-increasing invasion of privacy by CCTV cameras and technologies, on par CCTV-aware solutions must exist that provide privacy, safety, and cybersecurity features. We argue that a first important step towards…

Autonomous driving remains a challenging task, particularly due to safety concerns. Modern vehicles are typically equipped with expensive sensors such as LiDAR, cameras, and radars to reduce the risk of accidents. However, these sensors…

Vision-aided wireless sensing is emerging as a cornerstone of 6G mobile computing. While data-driven approaches have advanced rapidly, establishing a precise geometric correspondence between ego-centric visual data and radio propagation…

Imagine interconnected objects with embedded artificial intelligence (AI), empowered to sense the environment, see it, hear it, touch it, interact with it, and move. As future networks of intelligent objects come to life, tremendous new…

Cognitive (Radio) (CR) Communications (CC) are mainly deployed within the environments of primary (user) communications, where the channel states and accessibility are usually stochastically distributed (benign or IID). However, many…

Deep learning based image compressed sensing (CS) has achieved great success. However, existing CS systems mainly adopt a fixed measurement matrix to images, ignoring the fact the optimal measurement numbers and bases are different for…

Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient…

With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to…

In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of time and energy presents a formidable challenge. Conventional…