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Mixed Attention Densely Residual Network for Single Image Super-Resolution

computer Systems Science & engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

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Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals

arXiv

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arXiv 2023年

作者： Ding, Ruiqing Rong, Fangjie Han, Xiao Wang, Leye Key Lab of High Confidence Software Technologies Peking University Ministry of Education China School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China School of Mathematical Science Peking University Beijing China

There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis can be diagnosed by satisfying both suspicion of infection and Sequential Organ Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge, SofaNet adopts a multi-channel GRU structure to predict SOFA values of different systems, which can be seen as an auxiliary task to generate better health status representations for sepsis recognition. Moreover, we only achieve feature distribution alignment in the hidden space during cross-center collaborative learning, which ensures secure and compliant knowledge transfer without raw data exchange. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis recognition when hospitals only have limited EMRs. Copyright © 2023, The Authors. All rights reserved.

关键词： Electronic data interchange

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TAM: A Computing in Memory based on Tandem Array within STT-MRAM for Energy-Efficient Analog MAC Operation

TAM: A Computing in Memory based on Tandem Array within STT-...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jinkai Wang Zhengkun Gu Hongyu Wang Zuolei Hao Bojun Zhang Weisheng Zhao Yue Zhang MIIT Key Laboratory of Spintronics Fert Beijing Institute School of Integrated Circuit Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Nanoelectronics Science and Technology Center Hefei Innovation Research Institute Beihang University Hefei China

Computing in memory (CIM) has been demonstrated promising for energy efficient computing. However, the dramatic growth of the data scale in neural network processors has aroused a demand for CIM architecture of higher bit density, for which the spin transfer torque magnetic RAM (STT-MRAM) with high bit density and performance arises as an up-and-coming candidate solution. In this work, we propose an analog CIM scheme based on tandem array within STT-MRAM (TAM) to further improve energy efficiency while achieving high bit density. First, the resistance summation based analog MAC operation minimizes the effect of low tunnel magnetoresistance (TMR) by the serial magnetic tunnel junctions (MTJs) structure in the proposed tandem array with smaller area overhead. Moreover, a read scheme of resistive-to-binary is designed to achieve the MAC results accurately and reliably. Besides, the data-dependent error caused by MTJs in series has been eliminated with a proposed dynamic selection circuit. Simulation results of a 2Kb TAM architecture show 113.2 TOPS/W and 63.7 TOPS/W for 4-bit and 8-bit input/weight precision, respectively, and reduction by 39.3% for bit-cell area compared with existing array of MTJs in series.

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AUTOMATICALLY ADJUSTABLE MULTI-SCALE FEATURE EXTRACTION FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

AUTOMATICALLY ADJUSTABLE MULTI-SCALE FEATURE EXTRACTION FRAM...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Yang, Jiaqi Du, Bo Wu, Chen Zhang, Liangpei State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

Recently, deep learning-based methods have shown the great potential in hyperspectral image (HSI) classification. Nevertheless, feature extraction by convolutional neural network (CNN) is often performed on only one scale, resulting in multi-scale information loss. To address this problem, in this paper, we propose an automatically adjustable multi-scale feature extraction framework (A2MFE-Framework) for hyperspectral classification, including a scale reference network and two scale transformation networks. With the well-designed architecture, A2MFE-Framework can not only extract multi-scale features, but also automatically change the network structure to match input features of different scales. Experimental results on two benchmark HSI datasets demonstrate that the A2MFE-Framework can better capture multi-scale features of different objects via an automatically adjustable feature extraction framework with higher classification accuracy compared with previous methods. ©2021 IEEE

关键词： Feature extraction

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Model LEGO: creating models like disassembling and assembling building blocks 24

Model LEGO: creating models like disassembling and assemblin...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Jing Gao Jingwen Ye Yang Gao Xingen Wang Zunlei Feng Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University Robotics Institute School of Computer Science Carnegie Mellon University Electrical and Computer Engineering National University of Singapore Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/.

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Relieving Long-tailed Instance Segmentation via Pairwise Class Balance

arXiv

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arXiv 2022年

作者： He, Yin-Yin Zhang, Peizhen Wei, Xiu-Shen Zhang, Xiangyu Sun, Jian State Key Laboratory for Novel Software Technology Nanjing University China MEGVII Technology China School of Computer Science and Engineering Nanjing University of Science and Technology China

Long-tailed instance segmentation is a challenging task due to the extreme imbalance of training samples among classes. It causes severe biases of the head classes (with majority samples) against the tailed ones. This renders "how to appropriately define and alleviate the bias" one of the most important issues. Prior works mainly use label distribution or mean score information to indicate a coarse-grained bias. In this paper, we explore to excavate the confusion matrix, which carries the fine-grained misclassification details, to relieve the pairwise biases, generalizing the coarse one. To this end, we propose a novel Pairwise Class Balance (PCB) method, built upon a confusion matrix which is updated during training to accumulate the ongoing prediction preferences. PCB generates fightback soft labels for regularization during training. Besides, an iterative learning paradigm is developed to support a progressive and smooth regularization in such debiasing. PCB can be plugged and played to any existing method as a complement. Experimental results on LVIS demonstrate that our method achieves state-of-the-art performance without bells and whistles. Superior results across various architectures show the generalization ability. The code and trained models are available at https://***/megvii-research/PCB. Copyright © 2022, The Authors. All rights reserved.

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Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

arXiv

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arXiv 2024年

作者： Song, Ziying Liu, Lin Jia, Feiyang Luo, Yadan Jia, Caiyan Zhang, Guoxin Yang, Lei Wang, Li School of Computer Science & Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Information Technology and Electrical Engineering The University of Queensland St LuciaQLD4072 Australia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Intelligent Green Vehicle and Mobility The School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China

In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning. The key step to this system is related to 3D object detection that utilizes vehicle-mounted sensors such as LiDAR and cameras to identify the size, the category, and the location of nearby objects. Despite the surge in 3D object detection methods aimed at enhancing detection precision and efficiency, there is a gap in the literature that systematically examines their resilience against environmental variations, noise, and weather changes. This study emphasizes the importance of robustness, alongside accuracy and latency, in evaluating perception systems under practical scenarios. Our work presents an extensive survey of camera-only, LiDAR-only, and multimodal 3D object detection algorithms, thoroughly evaluating their trade-off between accuracy, latency, and robustness, particularly on datasets like KITTI-C and nuScenes-C to ensure fair comparisons. Among these, multi-modal 3D detection approaches exhibit superior robustness, and a novel taxonomy is introduced to reorganize the literature for enhanced clarity. This survey aims to offer a more practical perspective on the current capabilities and the constraints of 3D object detection algorithms in real-world applications, thus steering future research towards robustness-centric advancements. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Unified Question Generation with Continual Lifelong Learning

arXiv

引用

arXiv 2022年

作者： Yuan, Wei Yin, Hongzhi He, Tieke Chen, Tong Wang, Qiufeng Cui, Lizhen School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of School of Computer Science and Engineering Southeast University Nanjing China School of Software Shandong University Jinan China

Question Generation (QG), as a challenging Natural Language Processing task, aims at generating questions based on given answers and context. Existing QG methods mainly focus on building or training models for specific QG datasets. These works are subject to two major limitations: (1) They are dedicated to specific QG formats (e.g., answer-extraction or multi-choice QG), therefore, if we want to address a new format of QG, a re-design of the QG model is required. (2) Optimal performance is only achieved on the dataset they were just trained on. As a result, we have to train and keep various QG models for different QG datasets, which is resource-intensive and ungeneralizable. To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats. Specifically, we first build a format-convert encoding to transform different kinds of QG formats into a unified representation. Then, a method named STRIDER (SimilariTy RegularIzed Difficult Example Replay) is built to alleviate catastrophic forgetting in continual QG learning. Extensive experiments were conducted on 8 QG datasets across 4 QG formats (answer-extraction, answer-abstraction, multi-choice, and boolean QG) to demonstrate the effectiveness of our approach. Experimental results demonstrate that our Unified-QG can effectively and continually adapt to QG tasks when datasets and formats vary. In addition, we verify the ability of a single trained Unified-QG model in improving 8 Question Answering (QA) systems' performance through generating synthetic QA data. © 2022, CC BY-NC-SA.

关键词： Extraction

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Survey on depth and RGB image-based 3D hand shape and pose estimation

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Virtual Reality & Intelligent Hardware 2021年第3期3卷 207-234页

作者： Lin HUANG Boshen ZHANG Zhilin GUO Yang XIAO Zhiguo CAO Junsong YUAN Department of Computer Science and Engineering State University of New York at BuffaloNY 14260USA YouTu Lab TencentShanghai 201101China Department of Computer Science Fu Foundation School of Engineering and Applied ScienceColumbia UniversityNY 10027USA National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

The field of vision-based human hand three-dimensional(3D)shape and pose estimation has attracted significant attention recently owing to its key role in various applications,such as natural human computer *** the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks(DNNs),numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose ***,the existence of complicated hand articulation,depth and scale ambiguities,occlusions,and finger similarity remain *** this study,we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D *** RGB-D cameras,hand datasets,and a performance analysis are also discussed to provide a holistic view of recent *** also discuss the research potential of this rapidly growing field.

关键词： Hand survey 3D hand pose estimation Hand shape reconstruction Hand-object interactions RGB-D cameras

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Zero-Shot Stance Detection via Sentiment-Stance Contrastive Learning

Zero-Shot Stance Detection via Sentiment-Stance Contrastive ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiaying Zou Xuechen Zhao Feng Xie Bin Zhou Zhong Zhang Lei Tian School of Computer National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Zero-shot stance detection (ZSSD) is an important research problem that requires algorithms to have good stance detection capability even for unseen targets. In general, stance features can be grouped into two types: target-invariant and target-specific. Target-invariant features express the same stance regardless of the targets they are associated with, and such features are general and transferable. On the contrary, target-specific features will only be directly associated with specific targets. Therefore, it is crucial to effectively mine target-invariant features in texts in ZSSD. In this paper, we develop a method based on contrastive learning to mine certain transferable target-invariant expression features in texts from two dimensions of sentiment and stance and then generalize them to unseen targets. Specifically, we first grouped all texts into several types in terms of two orthogonal dimensions: sentiment polarity and stance polarity. Then we devise a supervised contrastive learning-based strategy to capture each type's common and transferable expressive features. Finally, we fuse the above-mentioned expressive features with the semantic features of the original texts about specific targets to deal with the stance detection for unseen targets. Extensive experiments on three benchmark datasets show that our proposed model achieves the state-of-the-art performance on most datasets. Code and other resources are available on GitHub 1 1 https://***/zoujiaying1995/sscl-project.

关键词： Training Analytical models Codes Fuses Semantics Benchmark testing Feature extraction

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