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Journal articles on the topic 'Multi-domain image translation'

Author: Grafiati

Published: 11 March 2023

Last updated: 31 July 2025

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1

Liu, Yahui, Yajing Chem, Linchao Bao, Nicu Sebe, Bruno Lepri, and Nadai Marco De. "ISF-GAN: An Implicit Style Function for High Resolution Image-to-Image Translation." IEEE TRANSACTIONS ON MULTIMEDIA 25 (September 1, 2023): 3343–53. https://doi.org/10.1109/TMM.2022.3159115.

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Recently, there has been an increasing interest in image editing methods that employ pre-trained unconditional image generators (e.g., StyleGAN). However, applying these methods to translate images to multiple visual domains remains challenging. Existing works do not often preserve the domain-invariant part of the image (e.g., the identity in human face translations), or they do not usually handle multiple domains or allow for multi-modal translations. This work proposes an implicit style function (ISF) to straightforwardly achieve multi-modal and multi-domain image-to-image translation from p

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Shao, Mingwen, Youcai Zhang, Huan Liu, Chao Wang, Le Li, and Xun Shao. "DMDIT: Diverse multi-domain image-to-image translation." Knowledge-Based Systems 229 (October 2021): 107311. http://dx.doi.org/10.1016/j.knosys.2021.107311.

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Liu, Huajun, Lei Chen, Haigang Sui, Qing Zhu, Dian Lei, and Shubo Liu. "Unsupervised multi-domain image translation with domain representation learning." Signal Processing: Image Communication 99 (November 2021): 116452. http://dx.doi.org/10.1016/j.image.2021.116452.

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Xue, Teng, Tianchi Zhang, and Jing Zhang. "Research on Underwater Image Restoration Technology Based on Multi-Domain Translation." Journal of Marine Science and Engineering 11, no. 3 (2023): 674. http://dx.doi.org/10.3390/jmse11030674.

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Underwater images are crucial in various underwater applications, including marine engineering, underwater robotics, and subsea coral farming. However, obtaining paired data for these images is challenging due to factors such as light absorption and scattering, suspended particles in the water, and camera angles. Underwater image recovery algorithms typically use real unpaired dataset or synthetic paired dataset. However, they often encounter image quality issues and noise labeling problems that can affect algorithm performance. To address these challenges and further improve the quality of un

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Cai, Naxin, Houjin Chen, Yanfeng Li, Yahui Peng, and Linqiang Guo. "Registration on DCE-MRI images via multi-domain image-to-image translation." Computerized Medical Imaging and Graphics 104 (March 2023): 102169. http://dx.doi.org/10.1016/j.compmedimag.2022.102169.

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Xia, Weihao, Yujiu Yang, and Jing-Hao Xue. "Unsupervised multi-domain multimodal image-to-image translation with explicit domain-constrained disentanglement." Neural Networks 131 (November 2020): 50–63. http://dx.doi.org/10.1016/j.neunet.2020.07.023.

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Zhang, Yifei, Weipeng Li, Daling Wang, and Shi Feng. "Unsupervised Image Translation Using Multi-Scale Residual GAN." Mathematics 10, no. 22 (2022): 4347. http://dx.doi.org/10.3390/math10224347.

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Image translation is a classic problem of image processing and computer vision for transforming an image from one domain to another by learning the mapping between an input image and an output image. A novel Multi-scale Residual Generative Adversarial Network (MRGAN) based on unsupervised learning is proposed in this paper for transforming images between different domains using unpaired data. In the model, a dual generater architecture is used to eliminate the dependence on paired training samples and introduce a multi-scale layered residual network in generators for reducing semantic loss of

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Yang, Benchen, Xuzhao Liu, Yize Li, Haibo Jin, and Yetian Qu. "Multi-attention bidirectional contrastive learning method for unpaired image-to-image translation." PLOS ONE 19, no. 4 (2024): e0301580. http://dx.doi.org/10.1371/journal.pone.0301580.

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Unpaired image-to-image translation (I2IT) involves establishing an effective mapping between the source and target domains to enable cross-domain image transformation. Previous contrastive learning methods inadequately accounted for the variations in features between two domains and the interrelatedness of elements within the features. Consequently, this can result in challenges encompassing model instability and the blurring of image edge features. To this end, we propose a multi-attention bidirectional contrastive learning method for unpaired I2IT, referred to as MabCUT. We design separate

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Shen, Yangyun, Runnan Huang, and Wenkai Huang. "GD-StarGAN: Multi-domain image-to-image translation in garment design." PLOS ONE 15, no. 4 (2020): e0231719. http://dx.doi.org/10.1371/journal.pone.0231719.

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Cai, Youcheng, Runshi Li, and Ligang Liu. "MV2MV: Multi-View Image Translation via View-Consistent Diffusion Models." ACM Transactions on Graphics 43, no. 6 (2024): 1–12. http://dx.doi.org/10.1145/3687977.

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Image translation has various applications in computer graphics and computer vision, aiming to transfer images from one domain to another. Thanks to the excellent generation capability of diffusion models, recent single-view image translation methods achieve realistic results. However, directly applying diffusion models for multi-view image translation remains challenging for two major obstacles: the need for paired training data and the limited view consistency. To overcome the obstacles, we present a first unified multi-view image to multi-view image translation framework based on diffusion

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Xu, Wenju, and Guanghui Wang. "A Domain Gap Aware Generative Adversarial Network for Multi-Domain Image Translation." IEEE Transactions on Image Processing 31 (2022): 72–84. http://dx.doi.org/10.1109/tip.2021.3125266.

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Komatsu, Rina, and Tad Gonsalves. "Multi-CartoonGAN with Conditional Adaptive Instance-Layer Normalization for Conditional Artistic Face Translation." AI 3, no. 1 (2022): 37–52. http://dx.doi.org/10.3390/ai3010003.

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In CycleGAN, an image-to-image translation architecture was established without the use of paired datasets by employing both adversarial and cycle consistency loss. The success of CycleGAN was followed by numerous studies that proposed new translation models. For example, StarGAN works as a multi-domain translation model based on a single generator–discriminator pair, while U-GAT-IT aims to close the large face-to-anime translation gap by adapting its original normalization to the process. However, constructing robust and conditional translation models requires tradeoffs when the computational

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Zhang, Xiyu, Xu Chen, Yang Wang, Dongliang Liu, and Yifeng Hong. "Source-Free Domain Adaptation for Cross-Modality Abdominal Multi-Organ Segmentation Challenges." Information 16, no. 6 (2025): 460. https://doi.org/10.3390/info16060460.

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Abdominal organ segmentation in CT images is crucial for accurate diagnosis, treatment planning, and condition monitoring. However, the annotation process is often hindered by challenges such as low contrast, artifacts, and complex organ structures. While unsupervised domain adaptation (UDA) has shown promise in addressing these issues by transferring knowledge from a different modality (source domain), its reliance on both source and target data during training presents a practical challenge in many clinical settings due to data privacy concerns. This study aims to develop a cross-modality ab

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Zhu, Zhifeng, Yaochen Li, Yifan Li, Jinhuo Yang, Peijun Chen, and Yuehu Liu. "SEIT: Structural Enhancement for Unsupervised Image Translation in Frequency Domain." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7820–27. http://dx.doi.org/10.1609/aaai.v38i7.28617.

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For the task of unsupervised image translation, transforming the image style while preserving its original structure remains challenging. In this paper, we propose an unsupervised image translation method with structural enhancement in frequency domain named SEIT. Specifically, a frequency dynamic adaptive (FDA) module is designed for image style transformation that can well transfer the image style while maintaining its overall structure by decoupling the image content and style in frequency domain. Moreover, a wavelet-based structure enhancement (WSE) module is proposed to improve the interm

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Fan, Runze. "Enhancing Semantic Consistency in Image-to-Image Translation with an Improved CycleGAN Framework." Applied and Computational Engineering 81, no. 1 (2024): 241–47. http://dx.doi.org/10.54254/2755-2721/81/20241216.

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Abstract. This paper presents an enhanced Cycle-Consistent Adversarial Networks (CycleGAN) model aimed at preserving semantic consistency during image-to-image translation, with a focus on complex tasks such as autonomous driving and scientific simulations. The study's key contribution is the incorporation of a pre-trained semantic segmentation model to preserve important characteristics during translation, such as license plates, traffic signs, and pedestrian structures. By introducing a semantic consistency loss alongside the traditional cycle-consistency loss, the proposed approach ensures

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Wu, Baolong, Haonan Wang, Cunle Zhang, and Jianlai Chen. "Optical-to-SAR Translation Based on CDA-GAN for High-Quality Training Sample Generation for Ship Detection in SAR Amplitude Images." Remote Sensing 16, no. 16 (2024): 3001. http://dx.doi.org/10.3390/rs16163001.

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Abundant datasets are critical to train models based on deep learning technologies for ship detection applications. Compared with optical images, ship detection based on synthetic aperture radar (SAR) (especially the high-Earth-orbit spaceborne SAR launched recently) lacks enough training samples. A novel cross-domain attention GAN (CDA-GAN) model is proposed for optical-to-SAR translation, which can generate high-quality SAR amplitude training samples of a target by optical image conversion. This high quality includes high geometry structure similarity of the target compared with the correspo

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Feng, Long, Guohua Geng, Qihang Li, Yi Jiang, Zhan Li, and Kang Li. "CRPGAN: Learning image-to-image translation of two unpaired images by cross-attention mechanism and parallelization strategy." PLOS ONE 18, no. 1 (2023): e0280073. http://dx.doi.org/10.1371/journal.pone.0280073.

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Unsupervised image-to-image translation (UI2I) tasks aim to find a mapping between the source and the target domains from unpaired training data. Previous methods can not effectively capture the differences between the source and the target domain on different scales and often leads to poor quality of the generated images, noise, distortion, and other conditions that do not match human vision perception, and has high time complexity. To address this problem, we propose a multi-scale training structure and a progressive growth generator method to solve UI2I task. Our method refines the generate

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Yu, Wenbo, Jiamu Li, Zijian Wang, and Zhongjun Yu. "Boosting SAR Aircraft Detection Performance with Multi-Stage Domain Adaptation Training." Remote Sensing 15, no. 18 (2023): 4614. http://dx.doi.org/10.3390/rs15184614.

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Deep learning has achieved significant success in various synthetic aperture radar (SAR) imagery interpretation tasks. However, automatic aircraft detection is still challenging due to the high labeling cost and limited data quantity. To address this issue, we propose a multi-stage domain adaptation training framework to efficiently transfer the knowledge from optical imagery and boost SAR aircraft detection performance. To overcome the significant domain discrepancy between optical and SAR images, the training process can be divided into three stages: image translation, domain adaptive pretra

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Tao, Rentuo, Ziqiang Li, Renshuai Tao, and Bin Li. "ResAttr-GAN: Unpaired Deep Residual Attributes Learning for Multi-Domain Face Image Translation." IEEE Access 7 (2019): 132594–608. http://dx.doi.org/10.1109/access.2019.2941272.

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Gómez, Jose L., Gabriel Villalonga, and Antonio M. López. "Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches." Sensors 21, no. 9 (2021): 3185. http://dx.doi.org/10.3390/s21093185.

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Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e.

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Xu, Xiaowe, Jiawei Zhang, Jinglan Liu, et al. "Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images." ACM Journal on Emerging Technologies in Computing Systems 17, no. 4 (2021): 1–16. http://dx.doi.org/10.1145/3462328.

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As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and

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Liu, Wenjie, Wenkai Zhang, Xian Sun, and Zhi Guo. "Unsupervised Cross-Scene Aerial Image Segmentation via Spectral Space Transferring and Pseudo-Label Revising." Remote Sensing 15, no. 5 (2023): 1207. http://dx.doi.org/10.3390/rs15051207.

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Unsupervised domain adaptation (UDA) is essential since manually labeling pixel-level annotations is consuming and expensive. Since the domain discrepancies have not been well solved, existing UDA approaches yield poor performance compared with supervised learning approaches. In this paper, we propose a novel sequential learning network (SLNet) for unsupervised cross-scene aerial image segmentation. The whole system is decoupled into two sequential parts—the image translation model and segmentation adaptation model. Specifically, we introduce the spectral space transferring (SST) approach to n

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Jin, Youngwan, Incheol Park, Hanbin Song, Hyeongjin Ju, Yagiz Nalcakan, and Shiho Kim. "Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation." Technologies 13, no. 4 (2025): 154. https://doi.org/10.3390/technologies13040154.

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This paper proposes Pix2Next, a novel image-to-image translation framework designed to address the challenge of generating high-quality Near-Infrared (NIR) images from RGB inputs. Our method leverages a state-of-the-art Vision Foundation Model (VFM) within an encoder–decoder architecture, incorporating cross-attention mechanisms to enhance feature integration. This design captures detailed global representations and preserves essential spectral characteristics, treating RGB-to-NIR translation as more than a simple domain transfer problem. A multi-scale PatchGAN discriminator ensures realistic

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Singh, Ayush, Yash Bhambhu, Himanshu Buckchash, Deepak K. Gupta, and Dilip K. Prasad. "Latent Graph Attention for Spatial Context in Light-Weight Networks: Multi-Domain Applications in Visual Perception Tasks." Applied Sciences 14, no. 22 (2024): 10677. http://dx.doi.org/10.3390/app142210677.

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Global contexts in images are quite valuable in image-to-image translation problems. Conventional attention-based and graph-based models capture the global context to a large extent; however, these are computationally expensive. Moreover, existing approaches are limited to only learning the pairwise semantic relation between any two points in the image. In this paper, we present Latent Graph Attention (LGA), a computationally inexpensive (linear to the number of nodes) and stable modular framework for incorporating the global context in existing architectures. This framework particularly empow

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Wang, Biao, Lingxuan Zhu, Xing Guo, Xiaobing Wang, and Jiaji Wu. "SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain." Remote Sensing 14, no. 6 (2022): 1359. http://dx.doi.org/10.3390/rs14061359.

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The synthesis of spectral remote sensing images of the Earth’s background is affected by various factors such as the atmosphere, illumination and terrain, which makes it difficult to simulate random disturbance and real textures. Based on the shared latent domain hypothesis and generation adversarial network, this paper proposes the SDTGAN method to mine the correlation between the spectrum and directly generate target spectral remote sensing images of the Earth’s background according to the source spectral images. The introduction of shared latent domain allows multi-spectral domains connect

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Sun, Pengyu, Miaole Hou, Shuqiang Lyu, et al. "Enhancement and Restoration of Scratched Murals Based on Hyperspectral Imaging—A Case Study of Murals in the Baoguang Hall of Qutan Temple, Qinghai, China." Sensors 22, no. 24 (2022): 9780. http://dx.doi.org/10.3390/s22249780.

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Environmental changes and human activities have caused serious degradation of murals around the world. Scratches are one of the most common issues in these damaged murals. We propose a new method for virtually enhancing and removing scratches from murals; which can provide an auxiliary reference and support for actual restoration. First, principal component analysis (PCA) was performed on the hyperspectral data of a mural after reflectance correction, and high-pass filtering was performed on the selected first principal component image. Principal component fusion was used to replace the origin

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Mizginov, V. A., V. V. Kniaz, and N. A. Fomin. "A METHOD FOR SYNTHESIZING THERMAL IMAGES USING GAN MULTI-LAYERED APPROACH." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-2/W1-2021 (April 15, 2021): 155–62. http://dx.doi.org/10.5194/isprs-archives-xliv-2-w1-2021-155-2021.

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Abstract. The active development of neural network technologies and optoelectronic systems has led to the introduction of computer vision technologies in various fields of science and technology. Deep learning made it possible to solve complex problems that a person had not been able to solve before. The use of multi-spectral optical systems has significantly expanded the field of application of video systems. Tasks such as image recognition, object re-identification, video surveillance require high accuracy, speed and reliability. These qualities are provided by algorithms based on deep convo

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Li, Yute, He Chen, Shan Dong, Yin Zhuang, and Lianlin Li. "Multi-Temporal SamplePair Generation for Building Change Detection Promotion in Optical Remote Sensing Domain Based on Generative Adversarial Network." Remote Sensing 15, no. 9 (2023): 2470. http://dx.doi.org/10.3390/rs15092470.

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Change detection is a critical task in remote sensing Earth observation for identifying changes in the Earth’s surface in multi-temporal image pairs. However, due to the time-consuming nature of image collection, labor-intensive pixel-level labeling with the rare occurrence of building changes, and the limitation of the observation location, it is difficult to build a large, class-balanced, and diverse building change detection dataset, which can result in insufficient changed sample pairs for training change detection models, thus degrading their performance. Thus, in this article, given that

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Suwanraksa, Chitchaya, Jidapa Bridhikitti, Thiansin Liamsuwan, and Sitthichok Chaichulee. "CBCT-to-CT Translation Using Registration-Based Generative Adversarial Networks in Patients with Head and Neck Cancer." Cancers 15, no. 7 (2023): 2017. http://dx.doi.org/10.3390/cancers15072017.

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Recently, deep learning with generative adversarial networks (GANs) has been applied in multi-domain image-to-image translation. This study aims to improve the image quality of cone-beam computed tomography (CBCT) by generating synthetic CT (sCT) that maintains the patient’s anatomy as in CBCT, while having the image quality of CT. As CBCT and CT are acquired at different time points, it is challenging to obtain paired images with aligned anatomy for supervised training. To address this limitation, the study incorporated a registration network (RegNet) into GAN during training. RegNet can dyna

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Zeng, Wei, and Mingbo Zhao. "High-Resolution Tiled Clothes Generation from a Model." AATCC Journal of Research 8, no. 1_suppl (2021): 97–104. http://dx.doi.org/10.14504/ajr.8.s1.13.

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Many image translation methods based on conditional generative adversarial networks can transform images from one domain to another, but the results of many methods are at a low resolution. We present a modified pix2pixHD model, which generates high-resolution tiled clothing from a model wearing clothes. We choose a single Markovian discriminator instead of a multi-scale discriminator for a faster training speed, added a perceptual loss term, and improved the feature matching loss. Deeper feature maps have lower weights when calculating losses. A dataset was specifically built for this improve

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Yang, Yuanbo, Qunbo Lv, Baoyu Zhu, Xuefu Sui, Yu Zhang, and Zheng Tan. "One-Sided Unsupervised Image Dehazing Network Based on Feature Fusion and Multi-Scale Skip Connection." Applied Sciences 12, no. 23 (2022): 12366. http://dx.doi.org/10.3390/app122312366.

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Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study of image dehazing is of great significance. Most existing unsupervised image-dehazing algorithms rely on a priori knowledge and simplified atmospheric scattering models, but the physical causes of haze in the real world are complex, resulting in inaccurate atmospheric scattering models that affect the dehazing effect. Unsupervised generative a

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Van den Broeck, Wouter A. J., Toon Goedemé, and Maarten Loopmans. "Multiclass Land Cover Mapping from Historical Orthophotos Using Domain Adaptation and Spatio-Temporal Transfer Learning." Remote Sensing 14, no. 23 (2022): 5911. http://dx.doi.org/10.3390/rs14235911.

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Historical land cover (LC) maps are an essential instrument for studying long-term spatio-temporal changes of the landscape. However, manual labelling on low-quality monochromatic historical orthophotos for semantic segmentation (pixel-level classification) is particularly challenging and time consuming. Therefore, this paper proposes a methodology for the automated extraction of very-high-resolution (VHR) multi-class LC maps from historical orthophotos under the absence of target-specific ground truth annotations. The methodology builds on recent evolutions in deep learning, leveraging domain

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Lu, Chien-Yu, Min-Xin Xue, Chia-Che Chang, Che-Rung Lee, and Li Su. "Play as You Like: Timbre-Enhanced Multi-Modal Music Style Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1061–68. http://dx.doi.org/10.1609/aaai.v33i01.33011061.

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Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domaininvariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Uns

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Guo, Hongjun, and Lili Chen. "An Image Similarity Invariant Feature Extraction Method Based on Radon Transform." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 288–96. http://dx.doi.org/10.46300/9106.2021.15.33.

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With the advancements of computer technology, image recognition technology has been more and more widely applied and feature extraction is a core problem of image recognition. In study, image recognition classifies the processed image and identifies the category it belongs to. By selecting the feature to be extracted, it measures the necessary parameters and classifies according to the result. For better recognition, it needs to conduct structural analysis and image description of the entire image and enhance image understanding through multi-object structural relationship. The essence of Rado

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Xu, Julin, and Fuqiang Zhou. "Study on the Effectiveness and Strategies of Chinese Cultural Dissemination in Russia." Communication across Borders: Translation & Interpreting 04, no. 01 (2024): 58–64. https://doi.org/10.5281/zenodo.12792069.

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The Belt and Road Initiative has emerged as a catalyst for the burgeoning cultural exchanges between China and Russia. Nevertheless, the Chinese cultural dissemination in Russia has encountered a few obstacles, thus affecting its effectiveness. This paper examines the typical problems associated with the Chinese cultural dissemination in Russia, as identified through a literature review and a questionnaire survey. The issues addressed include the image of Chinese culture, public engagement, the translation and Chinese cultural dissemination, and cultural identity. The paper proposes the adopti

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Islam, Naeem Ul, and Jaebyung Park. "Face Attribute Modification Using Fine-Tuned Attribute-Modification Network." Electronics 9, no. 5 (2020): 743. http://dx.doi.org/10.3390/electronics9050743.

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Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were based on either attribute-independent or attribute-dependent approaches. The attribute-independent approach, in which the modification is performed in the latent representation, has performance limitations because it requires paired data for changing the desired attributes. In contrast, the attribute-dependent approach is effective because it can modify the re

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Li, Shiping, Lianhui Liang, Shaoquan Zhang, Ying Zhang, Antonio Plaza, and Xuehua Wang. "End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification." Remote Sensing 16, no. 2 (2024): 325. http://dx.doi.org/10.3390/rs16020325.

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Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their limited receptive domain. The transformer can compensate well for this shortcoming, but it suffers from a lack of image-specific inductive biases (i.e., localization and translation equivariance) and contextual position information compared with CNNs. To overcome the aforementioned challenges, we introduce a simply structured, end-to-end convolutional

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Burlingame, Erik, Luke Ternes, Eun Na Kim, Joe W. Gray, and Young Hwan Chang. "Abstract 5431: 3D multiplexed tissue imaging reconstruction and optimized region-of-interest (ROI) selection through deep learning model of channels embedding." Cancer Research 83, no. 7_Supplement (2023): 5431. http://dx.doi.org/10.1158/1538-7445.am2023-5431.

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Abstract Tissue-based sampling and diagnosis is the extraction of information from specifically limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumo

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Xia, Haifei, Haiyan Zhou, Mingao Zhang, et al. "Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization." Sensors 25, no. 8 (2025): 2541. https://doi.org/10.3390/s25082541.

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Particleboard is an important forest product that can be reprocessed using wood processing by-products. This approach has the potential to achieve significant conservation of forest resources and contribute to the protection of forest ecology. Most current detection models require a significant number of tagged samples for training. However, with the advancement of industrial technology, the prevalence of surface defects in particleboard is decreasing, making the acquisition of sample data difficult and significantly limiting the effectiveness of model training. Deep reinforcement learning-bas

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Yue, Haixiao, Keyao Wang, Guosheng Zhang, et al. "Cyclically Disentangled Feature Translation for Face Anti-spoofing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3358–66. http://dx.doi.org/10.1609/aaai.v37i3.25443.

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Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary. However, it is usually difficult for these methods to achieve a perfect domain-invariant liveness feature disentanglement, which may degrade the final classification performance by domain differences in illumination, face category, spoof type, etc. In this work, we tackle cross-scenario face anti-spoofing by proposing a novel domain adaptation method called cyclically disentangled feature translation network (CDFTN)

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Zhang, Jianfu, Yuanyuan Huang, Yaoyi Li, Weijie Zhao, and Liqing Zhang. "Multi-Attribute Transfer via Disentangled Representation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9195–202. http://dx.doi.org/10.1609/aaai.v33i01.33019195.

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Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to

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Luo, Bingjun, Zewen Wang, Jinpeng Wang, Junjie Zhu, Xibin Zhao, and Yue Gao. "Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 565–73. http://dx.doi.org/10.1609/aaai.v38i1.27812.

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Illumination variation has been a long-term challenge in real-world facial expression recognition (FER). Under uncontrolled or non-visible light conditions, near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible (VIS) domain. Due to the lack of large-scale NIR facial expression datasets, directly extending VIS FER methods to the NIR spectrum may be ineffective. Additionally, previous heterogeneous image synthesis methods are restricted by low controllability without prior t

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Liu, Yue, and Xinbo Huang. "Efficient Cross-Modality Insulator Augmentation for Multi-Domain Insulator Defect Detection in UAV Images." Sensors 24, no. 2 (2024): 428. http://dx.doi.org/10.3390/s24020428.

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Regular inspection of the insulator operating status is essential to ensure the safe and stable operation of the power system. Unmanned aerial vehicle (UAV) inspection has played an important role in transmission line inspection, replacing former manual inspection. With the development of deep learning technologies, deep learning-based insulator defect detection methods have drawn more and more attention and gained great improvement. However, former insulator defect detection methods mostly focus on designing complex refined network architecture, which will increase inference complexity in rea

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Cortinhal, Tiago, and Eren Erdal Aksoy. "Depth- and semantics-aware multi-modal domain translation: Generating 3D panoramic color images from LiDAR point clouds." Robotics and Autonomous Systems 171 (January 2024): 104583. http://dx.doi.org/10.1016/j.robot.2023.104583.

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Jia, Meng, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, and Zhenghao Shi. "Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images." Remote Sensing 17, no. 15 (2025): 2581. https://doi.org/10.3390/rs17152581.

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Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder net

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Sommervold, Oscar, Michele Gazzea, and Reza Arghandeh. "A Survey on SAR and Optical Satellite Image Registration." Remote Sensing 15, no. 3 (2023): 850. http://dx.doi.org/10.3390/rs15030850.

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After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming and translating these characteristics into a shared domain has been the main challenge in SAR-optical matching for many years. Combining the two sensors will improve the quality of existing and future remote sensing applications across multiple industries. Several approaches have emerged as promising candidates in the search for combi

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Kou, Rong, Bo Fang, Gang Chen, and Lizhe Wang. "Progressive Domain Adaptation for Change Detection Using Season-Varying Remote Sensing Images." Remote Sensing 12, no. 22 (2020): 3815. http://dx.doi.org/10.3390/rs12223815.

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The development of artificial intelligence technology has prompted an immense amount of researches on improving the performance of change detection approaches. Existing deep learning-driven methods generally regard changes as a specific type of land cover, and try to identify them relying on the powerful expression capabilities of neural networks. However, in practice, different types of land cover changes are generally influenced by environmental factors at different degrees. Furthermore, seasonal variation-induced spectral differences seriously interfere with those of real changes in differe

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Roberts, Mitchell, Orli Kehat, Michaela Gross, et al. "TECHNOLOGY TRANSLATING THE RELATIONSHIP BETWEEN QUALITY OF LIFE AND MEMORY USING A NOVEL EEG TECHNOLOGY." Innovation in Aging 3, Supplement_1 (2019): S331—S332. http://dx.doi.org/10.1093/geroni/igz038.1207.

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Abstract Existing research has postulated a relationship between cognition and quality of life (QoL). Components of QoL such as satisfaction with social support may be particularly influential in memory for those with comorbidities. Additional research is needed to characterize the relationship between memory and QoL domains. Findings are presented from a clinical trial using BNA memory scores to assess brain health. BNA uses EEG technology and machine learning to map networks of brain functioning including working memory. Participants were older adults living in The Villages, an active lifest

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Monroe, Jonathan. "Urgent Matter." Konturen 8 (October 9, 2015): 8. http://dx.doi.org/10.5399/uo/konturen.8.0.3697.

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Opening questions about “things” onto the bureaucratically-maintained, compartmentalized discursive, disciplinary claims of “philosophy,” “theory,” and “poetry,” “Urgent Matter” explores these three terms in relation to one another through attention to recent work by Giorgio Agamben, Jacques Rancière, the German-American poet Rosmarie Waldrop, and the German poet Ulf Stolterfoht, whose fachsprachen. Gedichte. I-IX (Lingos I-IX. Poems) Waldrop rendered into English in an award-winning translation. The difference between the "things" called "poetry" and "philosophy," as now institutionalized wit

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Kim, Hyunjong, Gyutaek Oh, Joon Beom Seo, et al. "Multi-domain CT translation by a routable translation network." Physics in Medicine & Biology, September 26, 2022. http://dx.doi.org/10.1088/1361-6560/ac950e.

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Abstract Objective. To unify the style of CT images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector. Approach. Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network. Main results. Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging p

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