检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Jin, Zhedong Xia, Guiyu Yang, Paike Wang, Mengxiang Sun, Yubao Liu, Qingshan Jiangsu Key Laboratory of Big Data Analysis Technology School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China

Generating high-quality and diverse human images solely based on textual prompts is a challenging task in computer vision and computer graphics. Existing models do not have stable generation capabilities, and their control over image generation is insufficient, requiring an additional densepose map to provide pose information. In this work, we present a text-driven human image generation model, which enables control over texture and pose through textual input. The framework consists of three stages: 1) generating a pose map from the pose description which achieves control over the pose information of the generated human image. 2) converting the pose map to a parsing map based on the shape description, and 3) generating the final image by using textual descriptions of clothes texture along with a quality enhancement network. For the texture learning stage, we construct a texture-aware codebook using FSQ, learning vector representations for different clothes fabrics and patterns. Furthermore, we fine-tune the diffusion model to equip it with the capability of image denoising and image refining, thus improving the quality of the generated images. Extensive experimental results demonstrate our model's ability of generating realistic human body images that align with the semantics of the input text. © 2024, The Authors. All rights reserved.

关键词： Textures

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E2VIDiff: Perceptual Events-to-Video Reconstruction using Diffusion Priors

arXiv

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arXiv 2024年

作者： Liang, Jinxiu Yu, Bohan Yang, Yixin Han, Yiming Shi, Boxin National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Software and Microelectronics Peking University China

Event cameras, mimicking the human retina, capture brightness changes with unparalleled temporal resolution and dynamic range. Integrating events into intensities poses a highly ill-posed challenge, marred by initial condition ambiguities. Traditional regression-based deep learning methods fall short in perceptual quality, offering deterministic and often unrealistic reconstructions. In this paper, we introduce diffusion models to events-to-video reconstruction, achieving colorful, realistic, and perceptually superior video generation from achromatic events. Powered by the image generation ability and knowledge of pretrained diffusion models, the proposed method can achieve a better trade-off between the perception and distortion of the reconstructed frame compared to previous solutions. Extensive experiments on benchmark datasets demonstrate that our approach can produce diverse, realistic frames with faithfulness to the given events. © 2024, CC BY-NC-ND.

关键词： Cameras

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Semi-supervised Medical Image Segmentation with Distribution Calibration and Non-local Semantic Constraint

Semi-supervised Medical Image Segmentation with Distribution...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhang, Nan Hou, Junlin Zhao, Rui-Wei Feng, Rui Zhang, Yuejie Fudan University Academy for Engineering and Technology China Fudan University School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781665401265

The available medical images with accurate segmentation masks are usually limited due to the expensive and time-consuming annotation cost. Many semi-supervised approaches tried to exploit a large amount of unlabeled data together with the small number of labeled data. However, their learned segmentation models can easily become overfitted on the biased labeled data, mainly because of the misalignment between the labeled and unlabeled data distribution. To address this challenging problem, we propose a novel semi-supervised model with distribution calibration and non-local semantic constraint for medical image segmentation. In specific, we explicitly calibrate the learned feature distributions of the labeled and unlabeled data to make them aligned. Meanwhile, we add a special nonlocal semantic loss to encourage the learned features to be more discriminative for the segmentation task at the same time. Consequently, our final segmentation networks have the advantage to better generalize on the unlabeled data in both training and test set. Experimental results on three popular medical image segmentation benchmarks demonstrate that our proposed model achieves superior performance over other state-of-the-art methods. We will release our source code in this URL. © 2021 IEEE.

关键词： Calibration

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TEMPORALLY COARSE TO FINE SNIPPETS RELATIONSHIP LEARNING WITH GRAPH CONVOLUTION FOR TEMPORAL ACTION PROPOSAL GENERATION

TEMPORALLY COARSE TO FINE SNIPPETS RELATIONSHIP LEARNING WIT...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Li, Shuaicheng Zhao, Rui-Wei Miao, Shuyu Feng, Rui Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Academy for Engineering and Technology Fudan University China

ISBN: (纸本)9781665438643

Previous works have shown that explicit snippets relationship modeling can be helpful for feature learning on untrimmed action videos. However, the snippets relationship learning in these methods are far from optimal in that they failed to consider the valuable temporally coarse-grained features, learnable soft relationship weights, and separate relationship learning in different temporal orders. To address this issue, we proposed a novel SGC-Block for improved snippet relationship learning, which enables the temporally coarse-to-fine soft valued snippet-wise relationship learning in different temporal directions. The SGC-Block constructs the snippets graph and explicitly models the (1) temporal relations (TPR);(2) coarse-grained snippet-wise relations (CSR);(3) fine-grained snippet-wise relations (FSR);and an additional (4) adaptive relations (ADR). Especially, the novel CSR is inspired by the feature pyramid pooling structure to obtain the coarse feature presentations in the temporal dimension. Experimental results showed that our proposed approach outperforms most state-of-the-art methods on the THUMOS14 and ActivityNet-1.3 benchmarks. © 2021 IEEE

关键词： Convolution

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Semi-supervised Segmentation and Quantization Algorithm for Infarct Size Measurement of Rat Heart Sections 8

Semi-supervised Segmentation and Quantization Algorithm for ...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhu, Xiaoke Cai, Yulong Chen, Xiaopan Hu, Qi Dong, Lingyun Yuan, Caihong School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University China

ISBN: (纸本)9781665450515

Segmentation of rat heart section images and infarct area quantization are essential components of infarct size measurement in processing data from animal experiments on ischemic heart disease. Previously, the task of measuring the infarct size of rat heart sections was performed manually by experienced researchers, which was very costly and time-consuming. In this paper, we propose a two-stage semi-supervised approach to solve the task of ischemic heart infarct size calculation in images of rat section images. The approach uses a semi-supervised semantic segmentation model and a semi-automated quantization algorithm to separate the differently stained regions of rat heart sections. We demonstrate the results of the approach on a rat heart section dataset, and experiments on this dataset emphasize the effectiveness of our approach compared to other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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What Linguistic information Does Reading Comprehension Require? 5th

What Linguistic Information Does Reading Comprehension Requi...

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5th China Conference on Knowledge Graph, and Semantic Computing, CCKS 2020

作者： Guan, Yong Li, Ru Guo, Shaoru School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Ministry of Education for Computation Intelligence and Chinese Information Processing Shanxi University Taiyuan China

ISBN: (纸本)9789811619632

Machine comprehension is one of the primary goals in Artificial Intelligence (AI) and Natural Language processing (NLP). Accessing the difficulty level of machine reading comprehension (MRC) questions is important for building accurate MRC systems. In order to tackle this problem, we propose a novel idea to access the difficulty level of MRC questions, according to the amount of linguistic information required to answer them. Specifically, we systematically analyze and compare the performance for each BERT layer representation per question type on MRC datasets, and highlighted the characteristics of the datasets according to linguistic information of different layers. Our extensive analysis suggests that the superficial categories (or question types) of MRC questions do not directly reflect their difficulty levels and that it is possible to analyze the MRC questions’ difficulty levels according to the amount of linguistic information required. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Linguistics

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Feature Directions Matter: Long-Tailed Learning via Rotated Balanced Representation 40

Feature Directions Matter: Long-Tailed Learning via Rotated ...

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40th International Conference on Machine Learning, ICML 2023

作者： Peifeng, Gao Xu, Qianqian Wen, Peisong Yang, Zhiyong Shao, Huiyang Huang, Qingming School of Computer Science and Technology UCAS Beijing China Key Laboratory of Intelligent Information Processing Inst. of Comput. Tech. CAS Beijing China Inst. of Info. Engin. CAS Beijing China School of Cyber Security UCAS Beijing China BDKM UCAS Beijing China Peng Cheng Laboratory Shenzhen China

There are some studies aiming to solve long-tailed classification from the perspective of feature learning. Recent work proposes to learn the balanced representation by fixing the linear classifier as Equiangular Tight Frame (ETF), since they argue what matters in classification is the structure of the feature, instead of their directions. Holding a different view, in this paper, we show that features with fixed directions may be harmful to the generalization of models, even if it is completely symmetric. To avoid this issue, we propose Representation-Balanced Learning Framework (RBL), which introduces orthogonal matrices to learn directions while maintaining the geometric structure of ETF. Theoretically, our contributions are two-fold: 1). we point out that the feature learning of RBL is insensitive toward training set label distribution, it always learns a balanced representation space. 2). we provide a generalization analysis of proposed RBL through training stability. To analyze the stability of the parameter with orthogonal constraint, we propose a novel training stability analysis paradigm, Two-Parameter Model Stability. Finally, our method is extremely simple in implementation but shows great superiority on several benchmark datasets. © 2023 Proceedings of Machine Learning Research. All rights reserved.

关键词： Stability

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Computing Flow Experience Based on Physiological Signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Flow simulation

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ASMNet:Action and Style-Conditioned Motion Generative Network for 3D Human Motion Generation

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Cyborg and Bionic Systems 2024年第1期5卷 699-708页

作者： Zongying Li Yong Wang Xin Du Can Wang Reinhard Koch Mengyuan Liu School of Artificial Intelligence Chongqing University of TechnologyChongqingChina Multimedia Information Processing Laboratory at the Department of Computer Science Kiel UniversityKielGermany Advanced Institute of Information Technology(AIIT) Peking UniversityHangzhouChina Hangzhou Linxrobot Co.Ltd. HangzhouChina National Key Laboratory of General Artificial Intelligence Peking UniversityShenzhen Graduate SchoolShenzhenChina

Extensive research has explored human motion generation,but the generated sequences are influenced by different motion *** instance,the act of walking with joy and sorrow evokes distinct effects on a character’s *** to the difficulties in motion capture with styles,the available data for style research are also *** address the problems,we propose ASMNet,an action and style-conditioned motion generative *** network ensures that the generated human motion sequences not only comply with the provided action label but also exhibit distinctive stylistic *** extract motion features from human motion sequences,we design a spatial temporal ***,we use the adaptive instance normalization layer to inject style into the target *** results are comparable to state-of-the-art approaches and display a substantial advantage in both quantitative and qualitative *** code is available at https://***/ZongYingLi/***.

关键词： action conditioned motion generative network style conditioned D human motion generation spatial temporal extractor style research motion capture human motion generationbut

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Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification 8

Point-Level and Set-Level Deep Representation Learning for C...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Hu, Jihui Ye, Pengfei Li, Danyang Dong, Lingyun Chen, Xiaopan Zhu, Xiaoke School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China

ISBN: (纸本)9781665450515

In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Infrared imaging

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