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arXiv 2023年

作者： Wang, Xiao Wu, Wentao Li, Chenglong Zhao, Zhicheng Chen, Zhe Shi, Yukai Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China School of Artificial Intelligence Anhui University Hefei230601 China School of Computing Engineering and Mathematical Sciences La Trobe University Australia School of Information Engineering Guangdong University of Technology Guangzhou China

Understanding vehicles in images is important for various applications such as intelligent transportation and self-driving system. Existing vehicle-centric works typically pre-train models on large-scale classification datasets and then fine-tune them for specific downstream tasks. However, they neglect the specific characteristics of vehicle perception in different tasks and might thus lead to suboptimal performance. To address this issue, we propose a novel vehicle-centric pre-training framework called VehicleMAE, which incorporates the structural information including the spatial structure from vehicle profile information and the semantic structure from informative high-level natural language descriptions for effective masked vehicle appearance reconstruction. To be specific, we explicitly extract the sketch lines of vehicles as a form of the spatial structure to guide vehicle reconstruction. The more comprehensive knowledge distilled from the CLIP big model based on the similarity between the paired/unpaired vehicle image-text sample is further taken into consideration to help achieve a better understanding of vehicles. A large-scale dataset is built to pre-train our model, termed Autobot1M, which contains about 1M vehicle images and 12693 text information. Extensive experiments on four vehicle-based downstream tasks fully validated the effectiveness of our VehicleMAE. The source code and pre-trained models will be released at https://***/EventAHU/VehicleMAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging

arXiv

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arXiv 2024年

作者： Yang, Jing Cheng, Jian Li, Cheng Fan, Wenxin Zou, Juan Wu, Ruoyou Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Peng Cheng Laboratory Guangdong Shenzhen China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Yuhang District Hangzhou China

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Challenge-Aware RGBT Tracking 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Li, Chenglong Liu, Lei Lu, Andong Ji, Qing Tang, Jin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9783030585426

ISBN: (纸本)9783030585419

RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking. In this paper, we propose a novel challenge-aware neural network to handle the modality-shared challenges (e.g., fast motion, scale variation and occlusion) and the modality-specific ones (e.g., illumination variation and thermal crossover) for RGBT tracking. In particular, we design several parameter-shared branches in each layer to model the target appearance under the modality-shared challenges, and several parameter-independent branches under the modality-specific ones. Based on the observation that the modality-specific cues of different modalities usually contains the complementary advantages, we propose a guidance module to transfer discriminative features from one modality to another one, which could enhance the discriminative ability of some weak modality. Moreover, all branches are aggregated together in an adaptive manner and parallel embedded in the backbone network to efficiently form more discriminative target representations. These challenge-aware branches are able to model the target appearance under certain challenges so that the target representations can be learnt by a few parameters even in the situation of insufficient training data. From the experimental results we will show that our method operates at a real-time speed while performing well against the state-of-the-art methods on three benchmark datasets. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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LasHeR: A large-scale high-diversity benchmark for RGBT tracking

arXiv

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arXiv 2021年

作者： Li, Chenglong Xue, Wanlin Jia, Yaqing Qu, Zhichen Luo, Bin Tang, Jin Sun, Dengdi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for short-term RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: https://***/mmic-lcl/Datasets-and-benchmark-code. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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A New Method for Predefined-Time Synchronization Control of Memristive Cohen-Grossberg Neural Networks

A New Method for Predefined-Time Synchronization Control of ...

引用

Memristive computing and applications (ICMCA), IEEE International Conference on

作者： Aidi Liu Wenyue Yu Lili Huang Jian Guo Tengfei Lei Hui Zhao School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory.

关键词：

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Modeling Fine-Grained Relations in Dynamic Space-Time Graphs for Video-Based Facial Expression Recognition

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IEEE Transactions on Affective computing 2025年

作者： Huang, Changqin Jiang, Fan Han, Zhongmei Huang, Xiaodi Wang, Shijin Zhu, Yanlai Jiang, Yunliang Hu, Bin Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Jinhua321004 China Charles Sturt University School of Computing Mathematics and Engineering AlburyNSW2640 Australia State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Hangzhou Hikvision Digital Technology Co. Ltd Education Business Department Hangzhou310051 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China Beijing Institute of Technology School of Medical Technology Beijing10081 China

Facial expressions in videos inherently mirror the dynamic nature of real-world facial events. Consequently, facial expression recognition (FER) should employ a dynamic graph-based representation to effectively capture the relational structure of facial expressions rather than relying on conventional grid or sequence methods. However, existing graph-based approaches have their limitations. Frame-level graph methods provide a coarse representation of the facial graph across time and space, while landmark-based graph methods need to introduce additional facial landmarks, resulting in a static graph structure. To address these challenges, we propose spatial-temporal relation-aware dynamic graph convolutional networks (ST-RDGCN). This fine-grained relation modeling approach enables the dynamic modeling of evolving facial expressions in videos through dynamic space-time graphs, eliminating the need for facial landmarks. ST-RDGCN encompasses three graph construction paradigms: dynamic independent space graph, dynamic joint space-time graph, and dynamic cross space-time graph. Furthermore, we propose a relation-aware space-time graph convolution (RSTG-Conv) operator to learn informative spatiotemporal correlations in dynamic space-time graphs. In extensive experimental evaluations, our ST-RDGCN demonstrates state-of-the-art performance on the five popular video-based FER datasets, achieving overall accuracy scores of 99.69%, 91.67%, 56.51%, 69.37%, and 49.03% on the CK+, Oulu-CASIA, AFEW, DFEW, and FERV39 k datasets, respectively. In particular, our ST-RDGCN outperforms the current best method by 3.6% in UAR on the most challenging FERV39 k dataset. Furthermore, our analysis reveals that the dynamic cross space-time graph scheme is the most effective among the three dynamic graph construction schemes. © 2010-2012 IEEE.

关键词： Graphic methods

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Federated Client-tailored Adapter for Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Hu, Guyue Song, Siyuan Kang, Yukun Yin, Zhu Zhao, Gangming Li, Chenglong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Internet Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Department of Computer Science The University of Hong Kong Hong Kong

Medical image segmentation in X-ray images is beneficial for computer-aided diagnosis and lesion localization. Existing methods mainly fall into a centralized learning paradigm, which is inapplicable in the practical medical scenario that only has access to distributed data islands. Federated Learning has the potential to offer a distributed solution but struggles with heavy training instability due to client-wise domain heterogeneity (including distribution diversity and class imbalance). In this paper, we propose a novel Federated Client-tailored Adapter (FCA) framework for medical image segmentation, which achieves stable and client-tailored adaptive segmentation without sharing sensitive local data. Specifically, the federated adapter stirs universal knowledge in off-the-shelf medical foundation models to stabilize the federated training process. In addition, we develop two client-tailored federated updating strategies that adaptively decompose the adapter into common and individual components, then globally and independently update the parameter groups associated with common client-invariant and individual client-specific units, respectively. They further stabilize the heterogeneous federated learning process and realize optimal client-tailored instead of sub-optimal global-compromised segmentation models. Extensive experiments on three large-scale datasets demonstrate the effectiveness and superiority of the proposed FCA framework for federated medical segmentation. © 2025, CC BY.

关键词： Federated learning

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Fusion Tree Network for RGBT Tracking

Fusion Tree Network for RGBT Tracking

引用

IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Zhiyuan Cheng Andong Lu Zhang Zhang Chenglong Li Liang Wang School of Computer Science and Technology Anhui University Hefei China Center for Research on Intelligent Perception and Computing NLPR CASIA Beijing China University of Chinese Academy of Sciences Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Artificial Intelligence Anhui University Hefei China

ISBN: (数字)9781665463829

ISBN: (纸本)9781665463836

RGBT tracking is often affected by complex scenes (i.e., occlusions, scale changes, noisy background, etc). Existing works usually adopt a single-strategy RGBT tracking fusion scheme to handle modality fusion in all scenarios. However, due to the limitation of fusion model capacity, it is difficult to fully integrate the discriminative features between different modalities. To tackle this problem, we propose a Fusion Tree Network (FTNet), which provides a multi-strategy fusion model with high capacity to efficiently fuse different modalities. Specifically, we combine three kinds of attention modules (i.e., channel attention, spatial attention, and location attention) in a tree structure to achieve multi-path hybrid attention in the deeper convolutional stages of the object tracking network. Extensive experiments are performed on three RGBT tracking datasets, and the results show that our method achieves superior performance among state-of-the-art RGBT tracking models.

关键词： Fuses Convolution Surveillance Aggregates Benchmark testing Object tracking Noise measurement

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HySparK: Hybrid Sparse Masking for Large Scale Medical Image Pre-Training

arXiv

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arXiv 2024年

作者： Tang, Fenghe Xu, Ronghao Yao, Qingsong Fu, Xueming Quan, Quan Zhu, Heqin Liu, Zaiyi Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China Institute of Computing Technology CAS Beijing100190 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou China

The generative self-supervised learning strategy exhibits remarkable learning representational capabilities. However, there is limited attention to end-to-end pre-training methods based on a hybrid architecture of CNN and Transformer, which can learn strong local and global representations simultaneously. To address this issue, we propose a generative pre-training strategy called Hybrid Sparse masKing (HySparK) based on masked image modeling and apply it to large-scale pre-training on medical images. First, we perform a bottom-up 3D hybrid masking strategy on the encoder to keep consistency masking. Then we utilize sparse convolution for the top CNNs and encode unmasked patches for the bottom vision Transformers. Second, we employ a simple hierarchical decoder with skip-connections to achieve dense multi-scale feature reconstruction. Third, we implement our pre-training method on a collection of multiple large-scale 3D medical imaging datasets. Extensive experiments indicate that our proposed pre-training strategy demonstrates robust transfer-ability in supervised downstream tasks and sheds light on HySparK’s promising prospects. The code is available at https://***/FengheTan9/HySparK. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Effect of Ti-Cr Filler on Microstructure, Mechanical Properties, and Thermal Shock Resistance of Tzm/Graphite Brazed Joints

SSRN

引用

SSRN 2023年

作者： Liu, Dong-Guang Zou, Jin-Xin Zhang, Peng Zhou, Si-Wei Liu, Hui Li, Ze-Qun Anhui Province Key Lab of Aerospace Structural Parts Forming Technology and Equipment Industry & Equipment Technology Hefei University of Technology Hefei230009 China School of Materials Science and Engineering Hefei University of Technology Hefei230009 China Anhui YaLan Seal Component Co. Ltd Chuzhou233200 China Fujian Provincial Key Laboratory of Advanced Materials Processing and Application Fujian University of Technology Fuzhou350118 China Intelligent Manufacturing Institute Hefei University of Technology Hefei230009 China

One series of TZM/Ti-40Cr/graphite brazed joints were successfully obtained by connecting TZM alloy and graphite with Ti-40Cr filler through brazing in controlled atmosphere. The brazed joints' microstructure, mechanical properties, and thermal shock properties were characterized by X-ray diffraction, scanning electron microscopy, and a 3D laser confocal measuring microscope. The results indicated that the interface microstructure of TZM/graphite brazed joints was graphite/Cr3C2/TiC/Ti-Cr(Mo)/Ti-Mo(Cr)/TZM, and the thickness of the brazing seam increases with increasing temperature. In contrast, the thickness of the carbide reaction layer near the graphite side gradually becomes thicker. The shear tests showed that the joint's strength increased with the temperature increase in a specific temperature range. Maximum shear strength of 26.5 MPa was reached when the joint with groove structure brazed at 1480°C. The effect of holding time on the joints was similar to that of temperature, but the thickness of the carbide reaction layer did not change with holding time. The optimal brazing process for TZM/Ti-40Cr/graphite brazed joints was to hold for 20 min at 1480°C. TZM/Ti-40Cr/graphite brazed joints were subjected to a laser thermal shock test. The results indicated that, at most, TZM/Ti-40Cr/graphite brazed joints could only withstand laser thermal shock of 20A current. When the current rose to 40A, the TZM/Ti-40Cr/graphite joint was penetrated by laser, and the damage depth reached -253.67 μm and -254.824 μm. © 2023, The Authors. All rights reserved.

关键词： Brazing

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