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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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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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Gaussian-Face: Talking Head Generation with Hybrid Density via 3D Gaussian Splatting

Gaussian-Face: Talking Head Generation with Hybrid Density v...

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

作者： Guanwen Feng Yilin Zhang Yunan Li Siyu Jin 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

In recent years, audio-driven neural radiance field (NeRF)-based talking head generation techniques have achieved impressive results. However, these methods still have some limitations, such as unsynchronized lip movements and visual jitter. Recently, 3D Gaussian splatting has gradually replaced NeRF. Compared to NeRF, 3D Gaussian offers notable advantages, including higher efficiency and better reconstruction quality. Based on this, we propose Gaussian-Face, an audio-driven Gaussian-based facial avatar. With just a few minutes of monocular video and audio, a high-fidelity, driveable facial avatar can be reconstructed within hours. To achieve this, we first use FLAME to obtain the 3D representation of the face and then design a Lip Motion Translator to map audio to 3D lip representations. To model higher-quality facial details, we propose a hybrid density modeling method that balances rendering speed and quality, enabling our approach to render high-fidelity facial avatars at more than 160 FPS. Project page: https://***/Gaussian-Face/

关键词： Visualization Three-dimensional displays Lips Avatars Fires Rendering (computer graphics) Neural radiance field Synchronization Speech processing Faces

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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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Sign-Mamba: Advanced Mamba-Based Sign Language Generation

Sign-Mamba: Advanced Mamba-Based Sign Language Generation

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

作者： Guanwen Feng Yilin Zhang Yunan Li An Liu 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

In the field of sign language generation, Transformer-based models have been widely studied, but their quadratic computational complexity poses challenges. State Space Models (SSMs), like Mamba, offer a promising alternative with efficient long-range interaction modeling and linear complexity. In this study, we propose a two-stage generative framework for sign language generation, called Sign-Mamba, based on the state selection mechanism of SSM. In the first stage, we designed a Mamba-based encoder-decoder architecture, where the encoder captures the latent space representation and a symmetric decoder reconstructs it back into sign language skeletal points. In the second stage, the sign language latent space predicted by the Mamba-based latent predictor is used as a condition to guide the reconstruction network in generating more precise skeletal point sequences. We conducted comprehensive experiments on the PHOENIX and How2Sign datasets, and the results indicate that Sign-Mamba demonstrates competitive performance in sign language generation tasks. Project page: https://***/Sign-Mamba/

关键词： Sign language Computational modeling Semantics computer architecture Signal processing Transformers Acoustics Decoding Speech processing Computational complexity

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Multi-feature Based Memory-Enhanced Knowledge Tracing 6th

Multi-feature Based Memory-Enhanced Knowledge Tracing

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6th International Conference on computer Science and Educational Informatization, CSEI 2024

作者： Sun, Youwei Liu, Ruyi Miao, Qiguang Lu, Zixiang Zhao, Peipei Li, Ronghan School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Xi’an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi’an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China

ISBN: (纸本)9789819637379

In the realm of intelligent education, knowledge tracing (KT) has emerged as a crucial tool for enhancing educational efficiency and enabling personalized learning. Traditional KT methods primarily rely on students’ historical response data to evaluate their mastery of specific skills and forecast future performance. However, focusing solely on basic interactions, skills and response outcomes, makes it challenging to capture the complexities of real learning environments and accurately reflect students’ genuine knowledge states. To address this issue, we propose an innovative multi-feature based memory-enhanced knowledge tracing (MMKT) method. The key innovation of the MMKT model lies in the introduction of ‘memory features’ and ‘interaction features’. Memory features account for features such as the interval between repeated responses, the total number of responses, and memory interference during the overall learning process. In contrast, interaction features include response time, number of attempts, and hints. To integrate these features, we employ a self-attention mechanism coupled with a memory enhancement module and a recurrent neural network. This approach effectively uncovers variations in knowledge states while balancing the influence of global and local interactions. Our experimental results from different public datasets emphasize the benefits of the MMKT model in predicting future performance, underscoring the significance of integrating multi-feature and real-world relevant memory improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation

arXiv

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arXiv 2025年

作者： Liu, Kang Ma, Zhuoqi Kang, Xiaolu Li, Yunan Xie, Kun Jiao, Zhicheng Miao, Qiguang School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xi’an China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an China Warren Alpert Medical School Brown University Providence United States

Automated radiology report generation offers an effective solution to alleviate radiologists’ workload. However, most existing methods focus primarily on single or fixed-view images to model current disease conditions, which limits diagnostic accuracy and overlooks disease progression. Although some approaches utilize longitudinal data to track disease progression, they still rely on single images to analyze current visits. To address these issues, we propose enhanced contrastive learning with Multi-view Longitudinal data to facilitate chest X-ray Report Generation, named MLRG. Specifically, we introduce a multi-view longitudinal contrastive learning method that integrates spatial information from current multi-view images and temporal information from longitudinal data. This method also utilizes the inherent spatiotemporal information of radiology reports to supervise the pre-training of visual and textual representations. Subsequently, we present a tokenized absence encoding technique to flexibly handle missing patient-specific prior knowledge, allowing the model to produce more accurate radiology reports based on available prior knowledge. Extensive experiments on MIMIC-CXR, MIMIC-ABN, and Two-view CXR datasets demonstrate that our MLRG outperforms recent state-of-the-art methods, achieving a 2.3% BLEU-4 improvement on MIMIC-CXR, a 5.5% F1 score improvement on MIMIC-ABN, and a 2.7% F1 RadGraph improvement on Two-view CXR. Copyright © 2025, The Authors. All rights reserved.

关键词： Radiology

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Global TPACK Framework Analysis from the Perspective of Knowledge Graph 24

Global TPACK Framework Analysis from the Perspective of Know...

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Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence

作者： Yingchan Hu Jincheng Zhou Dan Wang Qingna Pan Duo Yang Yexue Pan Tao Hai Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou China Key Laboratory of Industrial Automation and Machine Vision of Guizhou Province School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun Guizhou China Affiliated Middle School of Qiannan Normal University for Nationalities Duyun Guizhou China

ISBN: (纸本)9798400711732

Currently, TPACK, as a teacher guidance framework, has received widespread attention from scholars both domestically and internationally. In order to elucidate the current status, focal points, and evolutionary patterns of worldwide TPACK research, the present study utilized CiteSpace visualization analysis software and HistCite software for examining the authors, institutions, countries/regions, keywords, citation clustering, and citation emergence involved in TPACK research. Findings reveal that starting from 2009, there has been a steady increase in the annual publication volume of TPACK research, with close cooperation between research institutions and countries, but weak communication among researchers. Research hotspots include pediatric content knowledge, framework, packaging, and education; Multi angle measurement of TPACK, diversified definition of "situational" factors, emphasizing teacher teaching beliefs and teacher TPACK development may become a future development trend.

关键词： CiteSpace

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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