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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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Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Cross-View Meets Diffusion: Aerial Image Synthesis with Geom...

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2025 IEEE/CVF Winter Conference on Applications of computer vision, WACV 2025

作者： Arrabi, Ahmad Zhang, Xiaohan Sultani, Waqas Chen, Chen Wshah, Safwan University of Vermont Vermont Artificial Intelligence Lab Department of Computer Science United States Information Technology University Intelligent Machines Lab Pakistan University of Central Florida Center for Research in Computer Vision United States

ISBN: (纸本)9798331510831

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibil-ity. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multimodal cross-view dataset, namely VIGORv2, built upon VIGOR [64] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/AhmadArrabi/GPG2A. © 2025 IEEE.

关键词： Photointerpretation

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Torchhd: An Open Source Python Library to Support research on Hyperdimensional Computing and Vector Symbolic Architectures

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Journal of Machine Learning research 2023年第1期24卷 1-10页

作者： Heddes, Mike Nunes, Igor Vergés, Pere Kleyko, Denis Abraham, Danny Givargis, Tony Nicolau, Alexandru Veidenbaum, Alexander Department of Computer Science University of California IrvineCA92617 United States Intelligent Systems Lab Research Institutes of Sweden Kista164 40 Sweden

Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces. The commitment of the scientific community to aggregate and disseminate research in this particularly multidisciplinary area has been fundamental for its advancement. Joining these efforts, we present Torchhd, a high-performance open source Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and serves as an efficient foundation for further research and application development. The easy-to-use library builds on top of PyTorch and features state-of-the-art HD/VSA functionality, clear documentation, and implementation examples from well-known publications. Comparing publicly available code with their corresponding Torchhd implementation shows that experiments can run up to 100× faster. Torchhd is available at: https://***/hyperdimensional-computing/torchhd. ©2023 Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, and Alexander Veidenbaum.

关键词： Python

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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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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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A hypothetical comparative evaluation system for arctic indoors

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Frontiers of Architectural research 2025年第1期14卷 210-223页

作者： Seyed-Amin Tabatabaeifard Jean-Francois Lalonde Marc Hebert Andre Potvin Claude MH.Demers GRAP(Groupe de Recherche en Ambiances Physiques) Ecole d’architectureUniversite LavalQuebecCanada Computer Vision and Systems Lab Department of Electrical and Computer EngineeringUniversite LavalQuebecCanada CERVO Brain Research Centre Faculty of MedicineUniversite LavalQuebecCanada

This research presents an innovative approach to evaluating indoor spaces,combining qualitative attributes with numerical architectural metrics. A hypothetical comparative visualization system is introduced, utilizing HDR visual imaging and thermal imaging in 360° field of view across multiple indoor environments. The study aims to provide architectsand occupants with a user-friendly tool informing them about the primary considerations oftheir built spaces, with a specific focus on indoor environmental qualities in remote Arctic regions. Key inquiries delve into the efficacy of the spherical approach and the capacity ofcomparative visualization to offer insights into space quality. Preliminary experiments contrastindoor environments in terms of circadian lighting, thermal uniformity, and view access tooutside in the 360° field of view (VAR360). The resulting visualizations hold significance inintroducing an immersive approach for depicting specific non-visible environmental qualities,particularly in relation to the window characteristics of spaces. It demonstrates the integration of multiple environmental variables, both steady-state and temporal, from central pointswithin spaces, providing a comprehensive view over their non-visible qualities. These resultsshould be useful for researchers and practitioners within building sciences, computer vision,and photobiology, showcasing an out-of-the-box approach for categorizing indoor spaces basedon standards and human-environmental qualifications.

关键词： Environmental assessment 360°imaging Comparative systems Low-cost tools Built environments

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3D facial attractiveness prediction based on deep feature fusion

3D facial attractiveness prediction based on deep feature fu...

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作者： Liu, Yu Huang, Enquan Zhou, Ziyu Wang, Kexuan Liu, Shu College of Systems Engineering National University of Defense Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Changsha China

Facial attractiveness prediction is an important research topic in the computer vision community. It not only contributes to the development of interdisciplinary research in psychology and sociology, but also provides fundamental technical support for applications like aesthetic medicine and social media. With the advances in 3D data acquisition and feature representation, this paper aims to investigate the facial attractiveness from deep learning and three-dimensional perspectives. The 3D faces are first processed to unwrap the texture images and refine the raw meshes. The feature extraction networks for texture, point cloud, and mesh are then delicately designed, considering the characteristics of different types of data. A more discriminative face representation is derived by feature fusion for the final attractiveness prediction. During network training, the cyclical learning rate with an improved range test is introduced, so as to alleviate the difficulty in hyperparameter setting. Extensive experiments are conducted on a 3D FAP benchmark, where the results demonstrate the significance of deep feature fusion and enhanced learning rate in cooperatively facilitating the performance. Specifically, the fusion of texture image and point cloud achieves the best overall prediction, with PC, MAE, and RMSE of 0.7908, 0.4153, and 0.5231, respectively. © 2023 John Wiley & Sons Ltd.

关键词： Forecasting

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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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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data

arXiv

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arXiv 2024年

作者： Lin, Zhi-Yi Lyu, Bofan Fernandez, Judith Cueto van der Kruk, Eline Seth, Ajay Zhang, Xucong Computer Vision Lab Department of Intelligent Systems Delft University of Technology Netherlands Delft University of Technology Netherlands

Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement. Conventional marker-based motion capture is expensive in terms of financial investment, time, and the expertise required. Moreover, due to the scarcity of datasets with accurate annotations, existing markerless motion capture methods suffer from challenges including unreliable 2D keypoint detection, limited anatomic accuracy, and low generalization capability. In this work, we propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views with consideration of biomechanical prior and spatio-temporal information. To train the model, we create synthetic dataset ODAH with accurate kinematics annotations generated by aligning the body mesh from the SMPL-X model and a full-body OpenSim skeletal model. Our extensive experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods when evaluated across multiple datasets, revealing a promising direction for enhancing video-based human motion capture. © 2024, CC BY-NC-SA.

关键词： Kinematics

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