Article
Computer Science, Artificial Intelligence
Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun
Summary: The success of monocular depth estimation relies on large and diverse training sets. This study proposes tools and methods to mix different datasets and improve the performance of monocular depth estimation.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger R. Roth, Daguang Xu
Summary: Fully Convolutional Neural Networks (FCNNs) have been successful in medical image segmentation, but their limited ability to learn long-range dependencies is a challenge. Inspired by transformers in NLP, we propose a novel architecture called UNet Transformers (UNETR) to redefine volumetric medical image segmentation as a sequence prediction problem. By combining transformers and U-shaped network design in the encoder and decoder, we effectively capture global information and achieve semantic segmentation output.
2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022)
(2022)
Article
Computer Science, Artificial Intelligence
Iman Ahmadianfar, Ali Asghar Heidari, Saeed Noshadian, Huiling Chen, Amir H. Gandomi
Summary: This study presents the analysis and principle of an innovative optimizer called INFO, which utilizes the weighted mean method to optimize different problems. The results show that INFO outperforms other methods in terms of exploration and exploitation, and is capable of converging to satisfactory solutions in engineering problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Geochemistry & Geophysics
Hao Chen, Zipeng Qi, Zhenwei Shi
Summary: This study introduces a bitemporal image transformer (BIT) for efficient and effective change detection by modeling contexts in the spatial-temporal domain. The BIT model demonstrates superior performance and efficiency on three CD datasets, significantly outperforming the purely convolutional baseline model with lower computational costs.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Triantafyllos Afouras, Joon Son Chung, Andrew Senior, Oriol Vinyals, Andrew Zisserman
Summary: This work aims to recognize phrases and sentences spoken by a talking face, and compares two lip reading models while investigating their complementarity to audio speech recognition. The introduction of a new dataset and the superior performance of the trained models on a lip reading benchmark dataset are the key contributions.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Software Engineering
Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu
Summary: This paper provides a comprehensive survey of techniques for testing machine learning systems and analyzes trends and challenges in ML testing, offering promising research directions for the future.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Engineering, Civil
Wu Deng, Junjie Xu, Huimin Zhao, Yingjie Song
Summary: The paper introduces a three-objective gate allocation model to optimize passenger walking distances, balanced idle time, and efficient use of gates. An IPOQEA algorithm is proposed to efficiently solve the model, with validation on the effectiveness at Baiyun Airport.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He
Summary: Knowledge graph-based recommender systems have attracted considerable interest in recent years as a way to solve the challenges faced by traditional recommender systems. In this paper, a systematical survey of knowledge graph-based recommender systems is conducted, categorizing them into embedding-based, connection-based, and propagation-based methods. The paper also explores how these approaches utilize the knowledge graph for accurate and explainable recommendation, and proposes potential research directions in this field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Geochemistry & Geophysics
Jiaming Han, Jian Ding, Jie Li, Gui-Song Xia
Summary: Significant progress has been made in the past decade on detecting objects in aerial images. We propose a single-shot alignment network (S(2)A-Net) that consists of two modules to address the misalignment issue between anchors and convolutional features, improving the consistency between classification score and localization accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Review
Computer Science, Information Systems
Achilles D. Boursianis, Maria S. Papadopoulou, Panagiotis Diamantoulakis, Aglaia Liopa-Tsakalidi, Pantelis Barouchas, George Salahas, George Karagiannidis, Shaohua Wan, Sotirios K. Goudos
Summary: This paper investigates the applications of IoT and UAV technology in agriculture. The main principles of IoT technology, including intelligent sensors, IoT sensor types, networks, and protocols used in agriculture, as well as IoT applications and solutions in smart farming are described. The role of UAV technology in smart agriculture is also presented, analyzing its applications in various scenarios such as irrigation, fertilization, use of pesticides, weed management, plant growth monitoring, crop disease management, and field-level phenotyping. IoT and UAV technology are identified as two important technologies that transform traditional cultivation practices into a new perspective of intelligence in precision agriculture.
INTERNET OF THINGS
(2022)
Article
Computer Science, Artificial Intelligence
Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, Luc Van Gool
Summary: With the advent of deep learning, dense prediction tasks have significantly improved. Recent multi-task learning techniques have shown promising results by jointly tackling multiple tasks. This survey provides a comprehensive view on state-of-the-art deep learning approaches for multi-task learning in computer vision, with a focus on dense prediction tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wenguan Wang, Qiuxia Lai, Huazhu Fu, Jianbing Shen, Haibin Ling, Ruigang Yang
Summary: This paper provides a comprehensive survey on deep salient object detection (SOD), covering algorithm taxonomy, unsolved issues, and dataset evaluation. The research shows that deep learning algorithms have made significant progress in SOD, and investigates the performance under different attribute settings, the robustness to random input perturbations and adversarial attacks, and the generalization of existing datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Wei Wang, Xiaoyang Suo, Xiangyu Wei, Bin Wang, Hao Wang, Hong-Ning Dai, Xiangliang Zhang
Summary: Graph Auto-Encoder is a framework for unsupervised learning on graph-structured data. However, it is not applicable for heterogeneous graphs that contain more abundant semantic information. Therefore, this work proposes a novel HGATE method for unsupervised representation learning on heterogeneous graph-structured data.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Sang-Min Park, Young-Gab Kim
Summary: This paper discusses the Metaverse based on the social value of Generation Z, analyzing its technological development, conceptual definition, and implementation methods in detail. A new definition of Metaverse is proposed, dividing the key concepts and technologies necessary for realizing Metaverse, as well as the impacts and challenges.
Article
Engineering, Electrical & Electronic
Fan Liu, Yuanhao Cui, Christos Masouros, Jie Xu, Tony Xiao Han, Yonina C. Eldar, Stefano Buzzi
Summary: This article provides a comprehensive review on the background, key applications, and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). It discusses the interplay between sensing and communications (S&C), performance tradeoffs, and signal processing aspects of ISAC. The article also presents a vision on the deeper integration of S&C within the framework of perceptive networks and the potential integration of ISAC with other emerging communication technologies, highlighting their positive impacts on the future of wireless networks.
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Zhifei Li, Hai Liu, Zhaoli Zhang, Tingting Liu, Neal N. Xiong
Summary: This article proposes a novel heterogeneous GNNs framework based on attention mechanism to address the aggregation of complex graph data containing various types of entities and relations in KGs. By learning weight values to aggregate features from different relation-paths for embedding representation, it captures various types of semantic information and selectively aggregates informative features.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Review
Engineering, Electrical & Electronic
Alberto Libanori, Guorui Chen, Xun Zhao, Yihao Zhou, Jun Chen
Summary: Incorporating sensing and therapeutic capabilities into everyday textiles is crucial for personalized healthcare. The development of smart textiles, driven by miniaturized platform technologies, has led to the creation of compact, autonomous, and interconnected functional textiles. This review examines the different platform technologies, fabrication strategies, clinical scenarios, as well as the commercial and regulatory landscape of smart textiles for personalized healthcare.
NATURE ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, Dusit Niyato, Octavia Dobre, H. Vincent Poor
Summary: This article explores the emerging opportunities brought by 6G technologies in IoT networks and applications. It sheds light on fundamental 6G technologies and discusses their roles in various prospective IoT applications. The article highlights research challenges and potential directions for further research in this promising area.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Geochemistry & Geophysics
Sheng Fang, Kaiyu Li, Jinyuan Shao, Zhe Li
Summary: This letter proposes a densely connected siamese network (SNUNet-CD) for change detection, which alleviates the loss of localization information in deep layers and introduces ECAM for deep supervision. Experimental results show that the method achieves a better tradeoff between accuracy and calculation amount compared to other state-of-the-art change detection methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Computer Science, Artificial Intelligence
Yongrui Chen, Huiying Li, Guilin Qi, Tianxing Wu, Tenggou Wang
Summary: Query graph construction aims to generate a correct SPARQL query to answer natural language questions on a knowledge graph. Existing methods face challenges in handling complex questions including complicated SPARQL syntax, huge search space, and locally ambiguous query graphs. This paper proposes a novel end-to-end approach that leverages a unified graph grammar called AQG and hierarchical autoregressive decoding to construct query graphs effectively. Experimental results demonstrate that the proposed method significantly improves the state-of-the-art performance on complex KGQA benchmarks.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
