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30th ACM International Conference on Multimedia (MM)

作者： Cao, Yunning Ma, Ye Zhou, Min Liu, Chuanbin Xie, Hongtao Ge, Tiezheng Jiang, Yuning Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Beijing Peoples R China

ISBN: (纸本)9781450392037

Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant layout should consider both the intra-domain relationship within layout elements and the inter-domain relationship between layout elements and the image. However, most previous methods simply focus on image-content-agnostic layout generation, without leveraging the complex visual information from the image. To this end, we explore a novel paradigm entitled image-conditioned layout generation, which aims to add text overlays to an image in a semantically coherent manner. Specifically, we propose an Image-Conditioned variational Transformer (ICVT) that autoregressively generates various layouts in an image. First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images. Subsequently, we take them as building blocks of conditional variational autoencoder (CVAE), which demonstrates appealing diversity. Second, in order to alleviate the gap between layout elements domain and visual domain, we design a Geometry Alignment module, in which the geometric information of the image is aligned with the layout representation. In addition, we construct a large-scale advertisement poster layout designing dataset with delicate layout and saliency map annotations. Experimental results show that our model can adaptively generate layouts in the non-intrusive area of the image, resulting in a harmonious layout design.

关键词： image-conditioned layout generation conditional variational autoencoder Transformer cross attention

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A Specialized variational autoencoder for Cost-Efficient Pedestrian Trajectory Prediction

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年

作者： Li, Dongchen Lin, Zhimao Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

The prediction of pedestrian trajectories represents a crucial and widely discussed topic in the field of AI-driven traffic scenarios. The prediction of pedestrian trajectories is constrained by two factors. First, pedestrians do not have the same traffic rule constraints as vehicles. Second, the computational power of in-vehicle systems is limited. This renders the application of traditional methods challenging. Previous methods have been observed to utilize redundant information, which can result in feature imbalance and the potential for model overfitting. In light of these limitations, we propose a lightweight conditional variational autoencoder model with post-process (L-CVAE-P) for pedestrian prediction scenarios. The L-CVAE-P focuses on the efficient interaction of multidimensional features to achieve a comprehensive enhancement of the model for real-world use. The model is tested on two public datasets and achieved state-of-the-art performance, while maintaining efficiency. The experimental results demonstrate that our work has developed and optimized a pedestrian trajectory prediction model for practical applications. (c) 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： pedestrian trajectory conditional variational autoencoder temporal sequence prediction scenarios interaction multi-dimensional feature balance

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Uncertainty Inspired Underwater Image Enhancement 17th

Uncertainty Inspired Underwater Image Enhancement

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17th European Conference on Computer Vision (ECCV)

作者： Fu, Zhenqi Wang, Wu Huang, Yue Ding, Xinghao Ma, Kai-Kuang Xiamen Univ Xiamen 361005 Fujian Peoples R China Nanyang Technol Univ Singapore 639798 Singapore

ISBN: (纸本)9783031197963;9783031197970

A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we resolve UIE into distribution estimation and consensus process. We present a novel probabilistic network to learn the enhancement distribution of degraded underwater images. Specifically, we combine conditional variational autoencoder with adaptive instance normalization to construct the enhancement distribution. After that, we adopt a consensus process to predict a deterministic result based on a set of samples from the distribution. By learning the enhancement distribution, our method can cope with the bias introduced in the reference map labeling to some extent. Additionally, the consensus process is useful to capture a robust and stable result. We examined the proposed method on two widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our approach enables sampling possible enhancement predictions. Meanwhile, the consensus estimate yields competitive performance compared with state-of-theart UIE methods. Code available at https://***/zhenqifu/PUIENet.

关键词： Underwater image enhancement Deep learning Probabilistic network Adaptive instance normalization conditional variational autoencoder

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SECT: A Successively conditional Transformer for Controllable Paraphrase Generation

SECT: A Successively Conditional Transformer for Controllabl...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Xue, Tang Zhao, Yuran Yang, Chaoqi Liu, Gongshen Li, Xiaoyong Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Illinois Dept Comp Sci Champaign IL USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Paraphrase generation has consistently been a challenging area in the field of NLP. Despite the considerable achievements made by previous work, existing methods lack a flexible way to include multiple controllable attributes to enhance the diversity of paraphrased sentences. To overcome this challenge, we propose a Successively conditional Transformer (SECT) to tackle this task. SECT is based on a combination of conditional variational autoencoder (CVAE) and Transformer framework to generate diversified words. More specifically, our SECT deploys multi-head attention and memory gate mechanism to keep the interaction between each of the attributes and the corresponding encoder layer hidden state. To address the problem of absorbing flexible attributes, we apply a successive structure to our SECT, which enables the framework to couple the CVAE latent variables with the encoder layer hidden states progressively. In addition, our SECT is trained by minimizing a tailor-designed loss for producing paraphrased sentences as required. Finally, we conduct extensive experiments to substantiate the validity and effectiveness of our proposed model. The results show that SECT significantly outperforms the existing state-of-the-art approaches and generates more diverse paraphrased sentences.

关键词： paraphrase generation conditional variational autoencoder transformer successive structure

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Learning a Generative Motion Model From Image Sequences Based on a Latent Motion Matrix

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2021年第5期40卷 1405-1416页

作者： Krebs, Julian Delingette, Herve Ayache, Nicholas Mansi, Tommaso Univ Cote Azur Epione Team INRIA F-06902 Sophia Antipolis France Siemens Healthineers Digital Technol & Innovat Princeton NJ 08540 USA

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.

关键词： Strain Deformable models Probabilistic logic Tracking Image sequences Gaussian processes Data models Motion model deformable registration conditional variational autoencoder Gaussian process latent variable model motion interpolation motion simulation tracking

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Introducing synthetic pseudo panels: application to transport behaviour dynamics

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TRANSPORTATION 2021年第5期48卷 2493-2520页

作者： Borysov, Stanislav S. Rich, Jeppe Tech Univ Denmark DTU Dept Technol Management & Econ DK-2800 Lyngby Denmark

In this paper, a method to study travel behaviour dynamics by constructing detailed synthetic pseudo panels from repeated cross-sectional data is presented. The method is based on the modelling of a high-dimensional joint distribution of travel preferences conditional on detailed socio-economic profiles by using a conditional variational autoencoder (CVAE). The CVAE is a neural-network-based generative model which allows the modelling of very detailed joint and conditional distributions, potentially defined by dozens or even hundreds of attributes in a flexible non-parametric form. The proposed method is used to rank detailed cohorts of individuals into slow and fast movers with respect to the speed at which their travel behaviour change over time. This gives an interesting insight into the types of individuals who are easily motivated to change their behaviour as opposed to those who are less flexible. Specifically, we investigate the dynamics of transport preferences for a fixed pseudo panel of individuals from a large Danish cross-sectional data set covering the period from 2006 to 2016. The comparison of the travel preference distributions from 2006 and 2016 shows that the prototypical fast mover is a single young woman who lives in a large city, whereas the typical slow mover is a middle-aged man with high income from a nuclear family who lives in a detached house outside a city. However, given that it is possible to rank individuals across very detailed socio-economic classifications, many other relationships can be explored. Finally, the CVAE can be directly applied to the population synthesis problem in microsimulation by modelling the distribution of socio-economic profiles conditional on other variables.

关键词： Dynamics of behaviour Pseudo-panel analysis conditional variational autoencoder Deep learning Generative modelling Population synthesis

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Toward Sub-Surface Pore Prediction Capabilities for Laser Powder Bed Fusion Using Data Science

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2021年第7期143卷 071016页

作者： Ertay, Deniz Sera Kamyab, Shima Vlasea, Mihaela Azimifar, Zohreh Thanh Ma Rogalsky, Allan D. Fieguth, Paul Univ Waterloo Dept Mech & Mechatron Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Shiraz Univ Dept Comp Sci & Engn & Informat Technol Molasadra AveEngn Bldg 2 Shiraz 71557138 Iran

Achieving defect-free parts is traditionally challenging in laser powder bed fusion (LPBF). The mechanical properties of additively manufactured parts are highly affected by their density;as such, research in defect detection and pore prediction has gained significant interest. The process parameters, the powder characteristics, and the process environment conditions play an important role in defect occurrence. Moreover, the laser scan path affects density, especially at scan path discontinuities. In this work, the complex interaction between the process parameters and the scan path on the occurrence of subsurface pores is investigated. In the data preparation step, a synthetic data set is generated to model the melt pool morphology along the scan path. A secondary data set containing the pore space of the resulting parts is obtained via X-ray computed tomography (CT) and is registered with the synthetic data set. Machine learning models, namely, a conditional variational autoencoder (CVAE) and a Convolutional Neural Network (CNN), are then trained based on the input features to predict pore occurrence. The performance evaluation of both CNN and CVAE models on synthetic data indicates that the scan path and process parameters can be utilized in predicting pore locations. Quantitative results show that employing offline CT images a priori in training the CVAE, without the need to have CT information in the test phase, leads the CVAE model to superior performance over the CNN.

关键词： additive manufacturing pore space analysis X-ray computed tomography deep neural networks conditional variational autoencoder advanced materials and processing laser processes modeling and simulation process planning

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Domain Reconstruction and Resampling for Robust Salient Object Detection 22

Domain Reconstruction and Resampling for Robust Salient Obje...

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30th ACM International Conference on Multimedia (MM)

作者： Yan, Senbo Peng, Liang Yu, Chuer Yang, Zheng Liu, Haifeng Cai, Deng Zhejiang Univ State Key Lab CAD & CG Hangzhou Zhejiang Peoples R China FABU Inc Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781450392037

Salient Object Detection (SOD) aims at detecting the salient objects covering the whole natural scene. However, one of the main problems in SOD is data bias. Natural scenes vary greatly, while each image in the SOD dataset contains a specific scene. It means that each image is just a sampling point in a specific scene, which is not representative and causes serious sampling bias. Building larger datasets is one solution but costly to address the sampling bias. Our method regards the data distribution of natural scenes as a Gaussian Mixture Distribution, and each scene follows a sub-Gaussian distribution. Our main idea is to reconstruct the data distribution of each scene from the sampling images and then resample from the distribution domain. We represent a scene by a distribution instead of a fixed sampling image to reserve the sampling uncertainty in SOD. Specifically, we employ a Style conditional variational autoencoder (Style-CVAE) to reconstruct the data distribution from image styles and a Gaussian Randomize Attribute Filter (GRAF) to reconstruct data distribution from image attributes (such as lightness, saturation, hue, etc.). We resample the reconstructed data distribution according to the Gaussian probability density function and train the SOD model. Experimental results prove that our method outperforms 16 state-of-the-art methods on five benchmarks.

关键词： Salient Object Detection Domain Reconstruction conditional variational autoencoder

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Few-shot fatigue damage evaluation of aircraft structure using neural augmentation and deep transfer learning

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ENGINEERING FAILURE ANALYSIS 2023年 148卷

作者： Che, Changchang Wang, Huawei Xiong, Minglan Luo, Shici Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China

To solve the problems of few-shot samples, different structural degradation trends and poor damage evaluation effect in fatigue damage evaluation of aircraft structure, an intelligent eval-uation method based on neural augmentation and deep transfer learning (NA-DTL) is proposed in this paper. Firstly, the fatigue damage is divided into three risk levels according to the length of crack, and conditional variational autoencoder (CVAE) and one-dimensional convolutional neural network (1-DCNN) are constructed to form the neural augmentation model for collaborative optimization of augmentation network and classification network. Subsequently, CVAE is used to generate massive fatigue damage samples, which can provide data support for building of crack length evaluation model. In addition, model-based transfer learning method is applied for damage evaluation according to the trained 1-DCNN. The fatigue crack growth dataset of aircraft aluminum lap joint is utilized to verify the effectiveness of the proposed method. The results show that the proposed method can achieve more accurate evaluation results compared with other models.

关键词： Fatigue damage evaluation Few-shot learning conditional variational autoencoder Neural augmentation Deep transfer learning

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Development of a surrogate model to improve the efficiency of groundwater level fluctuation pattern-based hydrologic properties evaluation

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JOURNAL OF HYDROLOGY 2023年 619卷

作者： Jeong, Jiho Jeong, Jina Kyungpook Natl Univ Dept Geol Daegu South Korea

In this study, a method of aquifer hydrologic property estimation incorporating the deep learning method was developed to improve the estimation efficiency of a process-based model based on groundwater level fluctuation (GLF) patterns. As a reference study, a data-driven method suggested by Jeong et al. (2020) was considered;the uncertainty of the GLF patterns resulting from different yearly patterns of precipitation, which were considered as noise in the previous study, was effectively discarded using the newly proposed method of applying the conditional variational autoencoder (CVAE). The CVAE was used to acquire the specific GLF patterns under certain identical precipitation patterns for all the monitoring stations. The data-driven hydrologic property estimation model was developed to predict two hydrologic parameters (rho and k) of the process-based model using the generated GLF patterns from the CVAE network as the input variables. The actual GLF and precipitation data that were acquired from nationwide groundwater monitoring stations in South Korea were applied to validate the developed method. It was found that the estimated and target hydrologic properties were highly correlated (correlation coefficients [CC]: 0.9833 and 0.9589 for rho and k, respectively), which significantly improved the results when compared to the previous study (CC: 0.7207 and 0.8663 for alpha/n and k, respectively). Consequently, the developed model can contribute to a more accurate hydrologic property estimation of aquifers. Additionally, it can facilitate efficient groundwater development planning since the manual fitting of the process-based model by an expert is not required.

关键词： Hydrologic property estimation Data-driven estimation model Groundwater level fluctuation pattern Precipitation pattern conditional variational autoencoder

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