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Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture

Linlin Shan, Long Zhang
2022 Sustainability  
To better capture the overall structure information and the style features of the local components in the architectural images, the group convolution and the dilated convolution were added into the ResNet  ...  model, and then, the improved channel attention mechanism was introduced to construct a novel CA-MSResNet model.  ...  not only pay attention to the overall external morphology of the architecture, but also to distinguishing the characteristics of the architectural detail elements.  ... 
doi:10.3390/su14127073 fatcat:espdmsihrzg2je7qrrm5dpucv4

Multideep Feature Fusion Algorithm for Clothing Style Recognition

Yuhua Li, Zhiqiang He, Sunan Wang, Zicheng Wang, Wanwei Huang, Amr Tolba
2021 Wireless Communications and Mobile Computing  
In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this  ...  The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch-normalized layer and the activation layer.  ...  They are no longer satisfied with the basic functional characteristics of covering up and heating and begin to pay attention to the aesthetics and personalized decorative characteristics of clothing [  ... 
doi:10.1155/2021/5577393 fatcat:b6hlhjkgevdrpkshokowlxdixi

ArCycleGAN: Improved CycleGAN for Style Transferring of Fruit Images

Hongqian Chen, Mengxi Guan, Hui Li
2021 IEEE Access  
LeafGAN [2] uses a new background similarity loss function, which makes the algorithm pay more attention to ROIs (Region of Interest), resulting in making the background of generated images as close  ...  ResNet [20] proposed a residual learning unit to solve the information loss problem that may occur in convolutional layer or fully connected layer.  ... 
doi:10.1109/access.2021.3068094 fatcat:o2habhckqrcarnmkaubnszx4pi

Monolingual and Cross-lingual Zero-shot Style Transfer [article]

Elizaveta Korotkova, Maksym Del, Mark Fishel
2018 arXiv   pre-print
Our model allows to increase the presence of dissimilar styles in corpus by up to 3 times, easily learns to operate with various contractions, and provides reasonable lexicon swaps as we see from manual  ...  We propose a unified multilingual multi-style machine translation system design, that allows to perform zero-shot style conversions during inference; moreover, it does so both monolingually and cross-lingually  ...  Acknowledgments The authors would like to thank the University for providing GPU computing resources.  ... 
arXiv:1808.00179v1 fatcat:lrn2iovlarb4vn6edvja4kmzve

Quantifying the Urban Visual Perception of Chinese Traditional-Style Building with Street View Images

Liying Zhang, Tao Pei, Xi Wang, Mingbo Wu, Ci Song, Sihui Guo, Yijin Chen
2020 Applied Sciences  
was quantified with two view indictors.  ...  Hence, it is important to study the visual perception of Chinese traditional-style buildings in China.  ...  Acknowledgments: Many thanks to the Tencent company for authorizing us to use Tencent street view pictures in this study. Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/app10175963 fatcat:sdfen7gaqralnj645kjawe2svu

APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation [article]

Yangming Shi, Haisong Ding, Kai Chen, Qiang Huo
2022 arXiv   pre-print
To achieve this goal, we propose 1) a content-style cross attention based pixel sampling approach to roughly mimicking the style text image's background; 2) a pixel-wise style modulation technique to transfer  ...  varying color patterns of the style image to the content image spatial-adaptively; 3) a cross attention based multi-scale style fusion approach to solving text foreground misalignment issue between style  ...  However, most previous works pay more attention to handling diverse glyphs while not considering complex style patterns in reality.  ... 
arXiv:2203.07705v1 fatcat:ayhnyy6zezcahjf2ncqvwi5g4e

3. Awaiting the Revolution: Surrealism Persian Style [chapter]

2020 Mute Dreams, Blind Owls, and Dispersed Knowledges  
Al-i Ahmad, while recognizing the dialogic nature of The Blind Owl-a conversation with the self in search of recollections and meaningful introspectionsnonetheless draws attention to details which reflect  ...  pay the Qur'an reader.  ... 
doi:10.1515/9780822385516-007 fatcat:ltqvitumzvdefa6is4gqeoy2ti

MHICNN model for fine-grained classification of product image

Mingxia Lin, Cuihua Li
2017 Advances in Modelling and Analysis B  
We use multi-scale to get more detail feature and heterogeneous integrate three models to achieve higher accuracy.  ...  Because women clothing contain more style, color and details than other clothes, in addition constructing a proper training set for these is very difficult.  ...  to the 3 3 × 3 patch. The purpose is to make the CNN model pay more attention to the local features.  ... 
doi:10.18280/ama_b.600108 fatcat:52w7dtgmivcj7osxzzp5ewxsa4

A State-of-the-Art Review on Image Synthesis with Generative Adversarial Networks

Lei Wang, Wei Chen, Wenjia Yang, Fangming Bi, Fei Richard Yu
2020 IEEE Access  
Then, we discuss the challenges faced by GANs and introduce some methods to deal with these problems.  ...  The purpose of this review is to provide insights into the research on GANs and to present the various applications based on GANs in different scenarios.  ...  It generates images guided with sketches and color by using an end-to-end trainable convolutional network and free-form user input with color and shape.  ... 
doi:10.1109/access.2020.2982224 fatcat:p5uxjh4cybfw5grp6ldhkpukrm

Structure-aware Image Inpainting with Two Parallel Streams [article]

Zhilin Huang, Chujun Qin, Ruixin Liu, Zhenyu Weng, Yuesheng Zhu
2021 arXiv   pre-print
In order to help SS focus on structures and prevent textures in MS from being affected, a gated unit is proposed to depress structure-irrelevant activations in the information flow between MS and SS.  ...  By interacting with SS in the training process, MS can be implicitly encouraged to exploit structural cues.  ...  For shallow encoders, GU usually pays attention to edge and contour information.  ... 
arXiv:2111.03414v1 fatcat:4cmwrcl6pfg2tddgpceoztjedi

Emulating Human Biases and Bounded Rationality and Advances in Ai/Ml

2019 International Journal of Engineering and Advanced Technology  
The paper concentrated on even more key questions regarding the interplay between theory and data in attempts to style as well as imitates human habits  ...  Machine learning, deep learning, as well as artificial intelligence all possess fairly particular meanings, yet are actually commonly extensively made use of to pertain to any type of modern, big-data  ...  For these causes, the ICRC has been advising Conditions to recognize sensible elements of individual management as the basis for worldwide acknowledged limits on autonomy in item bodies along with a pay  ... 
doi:10.35940/ijeat.f8701.088619 fatcat:7dljezm5jzh7dhlsrspccyk53i

ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network [article]

Liu Ying, Heng Fan, Fuchuan Ni, Jinhai Xiang
2020 arXiv   pre-print
accuracy and photo-realistic images.  ...  Considering that the editing images are prone to be affected by original attribute due to skip-connection in encoder-decoder structure, an upper convolution residual network (referred to as Tr-resnet)  ...  On the other hand, the attribute editing model may not be able to balance the effect between image quality and attribute transformation, because it needs to pay more attention to a lot of texture information  ... 
arXiv:1910.11764v2 fatcat:q2ywuh2zcrcgvlv3wdch5l6pce

A comprehensive survey on semantic facial attribute editing using generative adversarial networks [article]

Ahmad Nickabadi, Maryam Saeedi Fard, Nastaran Moradzadeh Farid, Najmeh Mohammadbagheri
2022 arXiv   pre-print
Generating random photo-realistic images has experienced tremendous growth during the past few years due to the advances of the deep convolutional neural networks and generative models.  ...  Among different domains, face photos have received a great deal of attention and a large number of face generation and manipulation models have been proposed.  ...  convolutional network that outputs the attention mask and the image map.  ... 
arXiv:2205.10587v1 fatcat:thpe4crcgndifb5mhtuveww4ji

MAANet: Multi-view Aware Attention Networks for Image Super-Resolution [article]

Jingcai Guo, Shiheng Ma, Song Guo
2019 arXiv   pre-print
Furthermore, we propose the local attentive residual-dense (LARD) block, which combines the LA attention with multiple residual and dense connections, to fit a deeper yet easy to train architecture.  ...  Specifically, we propose the local aware (LA) and global aware (GA) attention to deal with LR features in unequal manners, which can highlight the high-frequency components and discriminate each feature  ...  We construct With the GA attention, our MAANet can adaptively pay dense connections among the first three pairs of convolution unequal attention to each feature map in the global view  ... 
arXiv:1904.06252v1 fatcat:2q2b2xr7czgprmddh5u5salkte

TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models [article]

Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei
2021 arXiv   pre-print
In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image  ...  The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets.  ...  Standard Transformer architecture with the self-attention mechanism is leveraged on both encoder and decoder parts, where wordpiece units are generated as the recognized text from the input image.  ... 
arXiv:2109.10282v3 fatcat:3ra33gmd2vdgtect64gepyftni
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