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Edge-aware Feature Aggregation Network for Polyp Segmentation

Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Ultra-large mode area multi-core orbital angular momentum transmission fiber designed by neural network and optimization algorithms

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Optoelectronics Letters 2023年第12期19卷 744-751页

作者： GU Zhiwei HUANG Wei ZHANG Ran FAN Junjie SONG Binbin Key Laboratory of Computer Vision and Systems(Ministry of Education) School of Computer Science and EngineeringTianjin University of TechnologyTianjin 300384China Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

A large mode area multi-core orbital angular momentum(OAM)transmission fiber is designed and optimized by neural network and optimization *** neural network model has been established first to predict the optical properties of multi-core OAM transmission fibers with high accuracy and speed,including mode area,nonlinear coefficient,purity,dispersion,and effective index *** the trained neural network model is combined with different particle swarm optimization(PSO)algorithms for automatic iterative optimization of multi-core structures *** to the structural advantages of multi-core fiber and the automatic optimization process,we designed a number of multi-core structures with high OAM mode purity(>95%)and ultra-large mode area(>3000µm^(2)),which is larger by more than an order of magnitude compared to the conventional ring-core OAM transmission fibers.

关键词： network fiber OAM

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Guanwen Jin, Siyu Qian, Zhihao Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9798350368741

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KAN-Face, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker's identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN's sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/. © 2025 IEEE.

关键词： Face Animation KAN Network Low Resource Neural Radiance Fields Talking Head Generation

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Learning to see speckle in the weak laser field through multimode fiber

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Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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Small-scale refractive index sensing based on coreless microfiber operating near the turning around point 13

Small-scale refractive index sensing based on coreless micro...

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13th International Conference on Information Optics and Photonics, CIOP 2022

作者： Jiang, Yang Wu, Jixuan Bai, Hua Song, Binbin Zhang, Cheng Li, Ye Gao, Xinliang Huang, Kaixing Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems School of Electronic and Information Engineering Tiangong University Tianjin300387 China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781510660632

A coreless-microfiber sensor operating near the turning around point is proposed and experimentally demonstrated for small-scale refractive index (RI) sensing. Numerical simulations verify the existence of turning around point in the coreless-microfiber as the group effective RI difference between the HE11 and the HE12 modes equals zero. Experimental results show the RI sensitivity of the coreless-microfiber sensor could reach 4037.5nm/RIU around 1.333, which is consistent with the theoretical analysis results. Moreover, with the high sensing sensitivity and flexible design, the coreless-microfiber sensor operating near the turning around point has great significance application on molecular trace detection, especially in the fields of photochemical analysis and bioengineering. © 2022 SPIE.

关键词： Refractive index

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Sensitivity-enhanced refractive index sensor based on exposed-core single-hole microstructured fiber 13

Sensitivity-enhanced refractive index sensor based on expose...

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13th International Conference on Information Optics and Photonics, CIOP 2022

作者： Li, Ye Wu, Jixuan Song, Binbin Zhang, Cheng Gao, Xinliang Huang, Kaixing Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems School of Electronic and Information Engineering Tiangong University Tianjin300387 China Key Laboratory of Computer Vision and Systems Ministry of Education School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781510660632

Microstructured fibers with geometric versatility, and exceptional guiding properties, show unique advantage in constructing high-performance sensors. In this paper, a sensitivity-enhanced fiber-optic sensor based on an exposed-core single-hole microstructured fiber (SHMF) is proposed and experimentally demonstrated for the refractive index (RI) measurement of liquid samples. Experimental results show that the RI sensitivity of the exposed-core microstructured fiber is 375.77 nm/RIU in the low-range of 1.3409~1.3522. With the advantages of enhanced light-matter interaction ability, tight light confinement and mechanical robustness, the proposed sensor based on exposed-core single-hole microstructured fiber offers great potential in biological and chemical sensing applications. © 2022 SPIE.

关键词： Fiber optic sensors

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One-shot handwriting imitation via self-supervised cross spatial transformer networks

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Neurocomputing 2025年 642卷

作者： Zhao, Bocheng Feng, Guanwen Zhang, Wenxing Li, Yunan Miao, Qiguang Liu, Xiangzeng Liu, Ruyi School of Computer Science and Technology Xidian University Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Educational of Computer Science and Technology Xidian University Xi'an710071 China

Generating stylized characters has been a focus of research for a long time. However, current font-led algorithms in the field of handwriting generation often perform suboptimally due to severe geometric distortion among handwriting samples. Additionally, the non-Euclidean characteristic of hidden embedding is identified as another key issue in our research. To address shape distortion, we propose a Self-Supervised Style Learning (SSL) method, aiming to increase the skeleton similarity between the output image and target. Furthermore, to enhance the Euclidean spatial distributions of the generator, this paper introduces a joint spatial transformer by refining the fusion process of content and style information using an attention mechanism. Moreover, the training process of the current GAN is reformed to meet the requirements of SSL. As a result, the proposed method enriches the calligraphy diversity of stylized handwriting generation compared to existing font generation algorithms. Evaluation of the proposed method using a public Chinese handwriting database demonstrates that our approach generates handwriting images with superior quality, stroke correctness, and calligraphy similarity when compared to other prevailing font imitation methods. © 2025

关键词： Supervised learning

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Synthesis of wafer-scale graphdiyne/graphene heterostructure for scalable neuromorphic computing and artificial visual systems

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Nano Research 2021年第12期14卷 4591-4600页

作者： Zhi-Cheng Zhang Yi Li Jing-Jing Wang De-Han Qi Bin-Wei Yao Mei-Xi Yu Xu-Dong Chen Tong-Bu Lu Ministry of Education International Joint Laboratory of Materials Microstructure Institute for New Energy Materials and Low Carbon TechnologiesSchool of Material Science and EngineeringTianjin University of TechnologyTianjin300384China Key Laboratory of Computer Vision and Systems(Ministry of Education) School of Computer Science and EngineeringTianjin University of TechnologyTianjin300384China

Graphdiyne(GDY)is emerging as a promising material for various applications owing to its unique structure and fascinating ***,the application of GDY in electronics and optoelectronics are still in its infancy,primarily owing to the huge challenge in the synthesis of large-area and uniform GDY film for scalable *** a modified van der Waals epitaxy strategy is proposed to synthesize wafer-scale GDY film with high uniformity and controllable thickness directly on graphene(Gr)surface,providing an ideal platform to construct large-scale GDY/Gr-based optoelectronic synapse *** synaptic behaviors have been realized,and the linear and symmetric conductance-update characteristics facilitate the implementation of neuromorphic computing for image recognition with high accuracy and strong fault *** functions including“NAND”and“NOR”are integrated into the synapse which can be executed in an optical ***,a visible information sensing-memory-processing system is constructed to execute real-time image acquisition,in situ image memorization and distinction tasks,avoiding the time latency and energy consumption caused by data conversion and transmission in conventional visual *** results highlight the potential of GDY in applications of neuromorphic computing and artificial visual systems.

关键词： graphdiyne optoelectronic synapses neuromorphic computing sensing-memory-processing optical logic functions

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guanwen Feng Siyu Jin Zhihao Qian Yunan Li Qiguang Miao School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xidian University Xi’an China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KANFace, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker’s identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN’s sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/

关键词： Accuracy Three-dimensional displays Lips Color Signal processing Rendering (computer graphics) Feature extraction Synchronization Speech processing Faces

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