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629 results on '"Liu, Shiguang"'

1. SemiRefiner: Learning to Refine Semi-realistic Paintings

Author

Fan, Keyue, Liu, Shiguang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sheng, Bin, editor, Bi, Lei, editor, Kim, Jinman, editor, Magnenat-Thalmann, Nadia, editor, and Thalmann, Daniel, editor

Published
2024
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2. MFSFFuse: Multi-receptive Field Feature Extraction for Infrared and Visible Image Fusion Using Self-supervised Learning

Author

Gao, Xueyan, Liu, Shiguang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor

Published
2024
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5. An Overview of Color Transfer and Style Transfer for Images and Videos

Author

Liu, Shiguang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Image or video appearance features (e.g., color, texture, tone, illumination, and so on) reflect one's visual perception and direct impression of an image or video. Given a source image (video) and a target image (video), the image (video) color transfer technique aims to process the color of the source image or video (note that the source image or video is also referred to the reference image or video in some literature) to make it look like that of the target image or video, i.e., transferring the appearance of the target image or video to that of the source image or video, which can thereby change one's perception of the source image or video. As an extension of color transfer, style transfer refers to rendering the content of a target image or video in the style of an artist with either a style sample or a set of images through a style transfer model. As an emerging field, the study of style transfer has attracted the attention of a large number of researchers. After decades of development, it has become a highly interdisciplinary research with a variety of artistic expression styles can be achieved. This paper provides an overview of color transfer and style transfer methods over the past years., Comment: 12 pages, 13 figures

Published
2022

6. Two Decades of Colorization and Decolorization for Images and Videos

Author

Liu, Shiguang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Colorization is a computer-aided process, which aims to give color to a gray image or video. It can be used to enhance black-and-white images, including black-and-white photos, old-fashioned films, and scientific imaging results. On the contrary, decolorization is to convert a color image or video into a grayscale one. A grayscale image or video refers to an image or video with only brightness information without color information. It is the basis of some downstream image processing applications such as pattern recognition, image segmentation, and image enhancement. Different from image decolorization, video decolorization should not only consider the image contrast preservation in each video frame, but also respect the temporal and spatial consistency between video frames. Researchers were devoted to develop decolorization methods by balancing spatial-temporal consistency and algorithm efficiency. With the prevalance of the digital cameras and mobile phones, image and video colorization and decolorization have been paid more and more attention by researchers. This paper gives an overview of the progress of image and video colorization and decolorization methods in the last two decades., Comment: 12 pages, 19 figures

Published
2022

16. Binaural Audio Generation via Multi-task Learning

Author

Li, Sijia, Liu, Shiguang, and Manocha, Dinesh

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We present a learning-based approach for generating binaural audio from mono audio using multi-task learning. Our formulation leverages additional information from two related tasks: the binaural audio generation task and the flipped audio classification task. Our learning model extracts spatialization features from the visual and audio input, predicts the left and right audio channels, and judges whether the left and right channels are flipped. First, we extract visual features using ResNet from the video frames. Next, we perform binaural audio generation and flipped audio classification using separate subnetworks based on visual features. Our learning method optimizes the overall loss based on the weighted sum of the losses of the two tasks. We train and evaluate our model on the FAIR-Play dataset and the YouTube-ASMR dataset. We perform quantitative and qualitative evaluations to demonstrate the benefits of our approach over prior techniques.

Published
2021

20. Sound Synthesis, Propagation, and Rendering: A Survey

Author

Liu, Shiguang and Manocha, Dinesh

Subjects
Computer Science - Sound, Computer Science - Graphics
Abstract

Sound, as a crucial sensory channel, plays a vital role in improving the reality and immersiveness of a virtual environment, following only vision in importance. Sound can provide important clues such as sound directionality and spatial size. This paper gives a broad overview of research works on sound simulation in virtual reality, games, multimedia, computer-aided design. We first survey various sound synthesis methods, including harmonic synthesis, texture synthesis, spectral analysis, and physics-based synthesis. Then, we summarize popular sound propagation techniques, namely wave-based methods, geometric-based methods, and hybrid methods. Next, the sound rendering methods are reviewed. We further demonstrate the latest deep learning based sound simulation approaches. Finally, we point to some future directions of this field. To the best of our knowledge, this is the first attempt to provide a comprehensive summary of sound research in the field of computer graphics., Comment: 27 pages

Published
2020

27. Glucosylceramide synthase modulation ameliorates murine renal pathologies and promotes macrophage effector function in vitro

Author

Cheong, Agnes, Craciun, Florin, Husson, Hervé, Gans, Joseph, Escobedo, Javier, Chang, Yi-Chien, Guo, Lilu, Goncalves, Mariana, Kaplan, Nathan, Smith, Laurie A., Moreno, Sarah, Boulanger, Joseph, Liu, Shiguang, Saleh, Jacqueline, Zhang, Mindy, Blazier, Anna S., Qiu, Weiliang, Macklin, Andrew, Iyyanki, Tejaswi, Chatelain, Clément, Khader, Shameer, Natoli, Thomas A., Ibraghimov-Beskrovnaya, Oxana, Ofengeim, Dimitry, and Proto, Jonathan D.

Published
2024
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31. SemiPainter: Learning to Draw Semi-realistic Paintings from the Manga Line Drawings and Flat Shadow

Author

Fan, Keyue, Liu, Shiguang, Lu, Wenhuan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Zhang, Jian, editor, Kim, Jinman, editor, Papagiannakis, George, editor, Sheng, Bin, editor, Thalmann, Daniel, editor, and Gavrilova, Marina, editor

Published
2022
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37. Temporal-Consistency-Aware Video Color Transfer

Author

Liu, Shiguang, Zhang, Yu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Interrante, Victoria, editor, Thalmann, Daniel, editor, Papagiannakis, George, editor, Sheng, Bin, editor, Kim, Jinman, editor, and Gavrilova, Marina, editor

Published
2021
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38. Multi-task Deep Learning for No-Reference Screen Content Image Quality Assessment

Author

Gao, Rui, Huang, Ziqing, Liu, Shiguang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lokoč, Jakub, editor, Skopal, Tomáš, editor, Schoeffmann, Klaus, editor, Mezaris, Vasileios, editor, Li, Xirong, editor, Vrochidis, Stefanos, editor, and Patras, Ioannis, editor

Published
2021
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40. Artistic Style Transfer Based on Attention with Knowledge Distillation.

Author

Al‐Mekhlafi, Hanadi and Liu, Shiguang

Subjects
*ARTISTIC style, *GRAPHIC arts, *COMPUTER art, *GRAPHIC design, *DEEP learning
Abstract

Artistic style transfer involves the adaption of an input image to reflect the style of a reference image while maintaining its original content. This technique, now a prominent focus due to its prospective use in creative fields like digital art and graphic design, typically applies normalization techniques and attention mechanisms. While these methods yield decent results, they often fall short due to distortion of content image details and non‐artefact styles. In this paper, we introduce a novel approach that synergizes adaptive instance normalization (AdaIN), attention mechanisms, knowledge distillation (KD) and strategically placed internal layers, and new enhancements designed to preserve content details and provide a nuanced control over the style transfer process. We introduce a Detail Enhancement Module to amplify high‐frequency details in the content image, enhancing edge and texture preservation. A Multi‐scale Strategy is implemented to ensure uniform style application across various detail levels, leading to more coherent stylization. The Content Feature Refinement process refines content features, sharpening and emphasizing details to preserve structural and textural integrity. AdaIN's distinctive feature of efficiently collecting style data is exploited in our approach, coupled with attention mechanisms' inherent ability to conserve content information. We supplement this blend with KD for the enhancement of model accuracy and efficiency. Additionally, the introduction of internal layers acts as a conduit to further improve the style transfer process, increasing the transfer level of features and fostering better stylized results. The cornerstone of our technique lies in preserving the content structure amidst complex style transfers. Experimental results affirm the superior performance of our method over existing techniques in both quantitative and qualitative evaluations. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Expression of chemokine CXCL5 and programme death ligand 1 in colorectal cancer tissues are associated with prognosis of patients

Author

LIU Shiguang, ZHAO Jingkun, LU Aiguo, MAO Zhihai

Subjects
colorectal cancer, cxcl5, programmed death ligand 1, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Surgery, RD1-811
Abstract

Objective To analyze the expression of CXCL5 (C-X-C motif ligand 5) and programme death ligand 1 (PD-L1) in colorectal cancer tissues and explore two chemokines associated with clinical characteristics and prognosis. Methods The specimens of tumor tissues and adjacent normal tissues were collected from 78 patients with colorectal cancer who had laparoscopic resection from 2010 to 2011. The clinical data were gotten and follow-up were done for prognosis. Tissue microarrays were made from all specimens and immunohistochemical staining was performed. Expression of CXCL5 and PD-L1 was evaluated using immune risk scoring and analyzed with the association of clinical pathological parameters and prognosis of patients. Results Expression of CXCL5 and PD-L1 was associated with tumor size and TNM stage, and was higher in colorectal cancer tissues than in adjacent normal tissues. Cox univariate regression analysis showed that TNM stage, high expression of both CXCL5 and PD-L1 were the prognostic risk factors for overall survival of colorectal patients. It was shown with multivariate regression analysis that TNM stage and high expression of PD-L1 were the independent prognostic risk factors for overall survival rate. Patients with higher expression of both CXCL5 and PD-L1 had the worst prognosis. Conclusions CXCL5 combined with PD-L1 could stratify the risk of patients with colorectal cancer and predict the prognosis of patients.

Published
2021
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46. Interpolating Frames for Super-Resolution Smoke Simulation with GANs

Author

Wei, Wenguo, Liu, Shiguang, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tian, Feng, editor, Yang, Xiaosong, editor, Thalmann, Daniel, editor, Xu, Weiwei, editor, Zhang, Jian Jun, editor, Thalmann, Nadia Magnenat, editor, and Chang, Jian, editor

Published
2020
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47. Deep Partial Occlusion Facial Expression Recognition via Improved CNN

Author

Chen, Yujian, Liu, Shiguang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bebis, George, editor, Yin, Zhaozheng, editor, Kim, Edward, editor, Bender, Jan, editor, Subr, Kartic, editor, Kwon, Bum Chul, editor, Zhao, Jian, editor, Kalkofen, Denis, editor, and Baciu, George, editor

Published
2020
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48. Flow Visualization with Density Control

Author

Liu, Shiguang, Song, Hange, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Stephanidis, Constantine, editor, Wu, Enhua, editor, Thalmann, Daniel, editor, Sheng, Bin, editor, Kim, Jinman, editor, Papagiannakis, George, editor, and Gavrilova, Marina, editor

Published
2020
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49. Physics-Guided Sound Synthesis for Rotating Blades

Author

Xu, Siqi, Liu, Shiguang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Stephanidis, Constantine, editor, Wu, Enhua, editor, Thalmann, Daniel, editor, Sheng, Bin, editor, Kim, Jinman, editor, Papagiannakis, George, editor, and Gavrilova, Marina, editor

Published
2020
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