1. This is an Overview of How the Customer Support Team Created a Sorting System Using Data Analysis
    This article provides an in-depth look at how the customer support team utilized data analysis to create an efficient sorting system. It covers the challenges faced, the methodologies used, and the outcomes achieved.
  2. Small Is Beautiful
    This report explores the concept of "Small Is Beautiful" in the context of machine learning. It discusses the benefits of smaller models, their efficiency, and their potential applications.
  3. True TinyML with Weights & Biases: Wake Word Detection
    This report delves into the implementation of TinyML for wake word detection. It highlights the techniques used, performance metrics, and the practical applications of this technology.
  4. Vision Transformers for Image Quality Assessment
    This is a dissertation on the use of vision transformers for aesthetic image quality assessment. I won the best dissertation award for this and obtained near state of the art accuracy of the AVA dataset.
  1. Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing
  2. Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric
  3. Image Quality Assessment in The Essential Guide to Image Processing (Second Edition)
  4. No-reference quality assessment using natural scene statistics: JPEG2000
  5. The Design of High-Level Features for Photo Quality Assessment
  6. VTAMIQ: Transformers for Attention Modulated Image Quality Assessment
  7. A Comprehensive Survey on Image Aesthetic Quality Assessment
  8. AVA: A large-scale database for aesthetic visual analysis
    Murray, Naila and Marchesotti, Luca and Perronnin, Florent (2021)
  9. A comprehensive aesthetic quality assessment method for natural images using basic rules of photography
    Mavridaki, Eftichia and Mezaris, Vasileios (2015)
  10. A comprehensive aesthetic quality assessment method for natural images using basic rules of photography
    Mavridaki, Eftichia and Mezaris, Vasileios (2015)
  11. A Comprehensive Survey on Image Aesthetic Quality Assessment
    Yang, Hongtao and Shi, Ping and He, Saike and Pan, Da and Ying, Zefeng and Lei, Ling (2019)
  12. A Haar wavelet-based perceptual similarity index for image quality assessment
    Reisenhofer, Rafael and Bosse, Sebastian and Kutyniok, Gitta and Wiegand, Thomas (2018)
  13. A Novel Feature Fusion Method for Computing Image Aesthetic Quality
    Li, Xuewei and Li, Xueming and Zhang, Gang and Zhang, Xianlin (2020)
  14. A statistic approach for photo quality assessment
    Lo, Li-Yun and Chen, Ju-Chin (2012)
  15. A Survey of Hand Crafted and Deep Learning Methods for Image Aesthetic Assessment
    Kanwal, Saira and Uzair, Muhammad and Ullah, Habib (2021)
  16. A-Lamp: Adaptive Layout-Aware Multi-patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
    Ma, Shuang and Liu, Jing and Chen, Chang Wen (2017)
  17. Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
    Chen, Qiuyu and Zhang, Wei and Zhou, Ning and Lei, Peng and Xu, Yi and Zheng, Yu and Fan, Jianping (2020)
  18. Aesthetic Attributes Assessment of Images
    Jin, Xin and Wu, Le and Zhao, Geng and Li, Xiaodong and Zhang, Xiaokun and Ge, Shiming and Zou, Dongqing and Zhou, Bin and Zhou, Xinghui (2019)
  19. Aesthetic quality assessment of consumer photos with faces
    Li, Congcong and Gallagher, Andrew and Loui, Alexander C. and Chen, Tsuhan (2010)
  20. Assessing the aesthetic quality of photographs using generic image descriptors
    Marchesotti, Luca and Perronnin, Florent and Larlus, Diane and Csurka, Gabriela (2011)
  21. Assessment of Photo Aesthetics with Efficiency Research Center for Information Technology Innovation , Academia Sinica , Taipei , Taiwan Institute of Information Science , Academia Sinica , Taipei , Taiwan
    Lo, Kuo-yen and Liu, Keng-hao and Chen, Chu-song (2012)
  22. Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment
    Sheng, Kekai and Dong, Weiming and Ma, Chongyang and Mei, Xing and Huang, Feiyue and Hu, Bao-Gang (2018)
  23. Attention-based multi-patch aggregation for image aesthetic assessment
    Sheng, Kekai and Mei, Xing and Dong, Weiming and Huang, Feiyue and Ma, Chongyang and Hu, Bao Gang (2018)
  24. AVA: A large-scale database for aesthetic visual analysis
    Murray, Naila and Marchesotti, Luca and Perronnin, Florent (2012)
  25. Blind image quality assessment: a natural scene statistics approach in the DCT domain.
    Saad, Michele A and Bovik, Alan C and Charrier, Christophe (2012)
  26. Brain-Inspired Deep Networks for Image Aesthetics Assessment
    Wang, Zhangyang and Chang, Shiyu and Dolcos, Florin and Beck, Diane and Liu, Ding and Huang, Thomas S. (2016)
  27. Composition and Style Attributes Guided Image Aesthetic Assessment
    Celona, Luigi and Leonardi, Marco and Napoletano, Paolo and Rozza, Alessandro (2021)
  28. Composition-aware image aesthetics assessment
    Liu, Dong and Puri, Rohit and Kamath, Nagendra and Bhattacharya, Subhabrata (2020)
  29. Composition-Preserving Deep Photo Aesthetics Assessment
    Mai, Long and Jin, Hailin and Liu, Feng (2016)
  30. Composition-Preserving Deep Photo Aesthetics Assessment
    Mai, Long and Jin, Hailin and Liu, Feng (2016)
  31. Content-Based Photo Quality Assessment
    Tang, Xiaoou and Luo, Wei and Wang, Xiaogang (2013)
  32. Deep Aesthetic Quality Assessment with Semantic Information
    Kao, Yueying and He, Ran and Huang, Kaiqi (2016)
  33. Deep multi-patch aggregation network for image style, aesthetics, and quality estimation
    Lu, Xin and Lin, Zhe and Shen, Xiaohui and Mech, Radomir and Wang, James Z. (2015)
  34. Distribution-Oriented Aesthetics Assessment With Semantic-Aware Hybrid Network
    Cui, Chaoran and Liu, Huihui and Lian, Tao and Nie, Liqiang and Zhu, Lei and Yin, Yilong (2019)
  35. Hierarchical aesthetic quality assessment using deep convolutional neural networks
    Kao, Yueying and Huang, Kaiqi and Maybank, Steve (2016)
  36. Image Aesthetic Assessment Based on Image Classification and Region Segmentation
    Le, Quyet-tien and Ladret, Patricia and Nguyen, Huu-tuan and Caplier, Alice (2020)
  37. Image Aesthetic Assessment: An experimental survey
    Deng, Yubin and Loy, Chen Change and Tang, Xiaoou (2017)
  38. Image Aesthetic Quality Assessment Based on Deep Neural Networks To cite this version : HAL Id : tel-03159861
    Kang, Chen (2020)
  39. Image database TID2013: Peculiarities, results and perspectives
    Ponomarenko, Nikolay and Jin, Lina and Ieremeiev, Oleg and Lukin, Vladimir and Egiazarian, Karen and Astola, Jaakko and Vozel, Benoit and Chehdi, Kacem and Carli, Marco and Battisti, Federica and Jay Kuo, C. C. (2015)
  40. Image Quality Assessment
    Seshadrinathan, Kalpana and Pappas, Thrasyvoulos N. and Safranek, Robert J. and Chen, Junqing and Wang, Zhou and Sheikh, Hamid R. and Bovik, Alan C. (2009)
  41. Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric
    Gu, Jinjin and Cai, Haoming and Chen, Haoyu and Ye, Xiaoxing and Ren, Jimmy and Dong, Chao (2020)
  42. Image Quality Assessment: From Error Visibility to Structural Similarity
    Wang, Zhou and Bovik, A.C. and Sheikh, H.R. and Simoncelli, E.P. (2004)
  43. Intelligent Photographing Interface with On-Device Aesthetic Quality Assessment
    Lo, Kuo-Yen and Liu, Keng-Hao and Chen, Chu-Song (2013)
  44. Learning Quality, Aesthetics, and Facial Attributes for Image Annotation
    Celona, L (2018)
  45. Low Level Features for Quality Assessment of Facial Images
    Lienhard, Arnaud and Ladret, Patricia and Caplier, Alice (2015)
  46. Measuring the perceived aesthetic quality of photographic images
    Cerosaletti, Cathleen Daniels and Loui, Alexander C. and Jiang, Wei (2009)
  47. Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning
    Wu, Ou and Zuo, Haiqiang and Hu, Weiming and Li, Bing (2016)
  48. Multiple Aesthetic Attribute Assessment by Exploiting Relations Among Aesthetic Attributes
    Gao, Zhen and Wang, Shangfei and Ji, Qiang (2015)
  49. NIMA: Neural Image Assessment
    Talebi, Hossein and Milanfar, Peyman (2018)
  50. No-reference quality assessment using natural scene statistics: JPEG2000
    Sheikh, Hamid Rahim and Bovik, Alan Conrad and Cormack, Lawrence (2005)
  51. Perceptual quality assessment based on visual attention analysis
    You, Junyong and Perkis, Andrew and Hannuksela, Miska M. and Gabbouj, Moncef (2009)
  52. Personalised aesthetics assessment in photography using deep learning Carlos Rodr ´ ıguez - Pardo Master of Science Artificial Intelligence School of Informatics University of Edinburgh
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  53. Personality-assisted multi-task learning for generic and personalized image aesthetics assessment
    Li, Leida and Zhu, Hancheng and Zhao, Sicheng and Ding, Guiguang and Lin, Weisi (2020)
  54. Relative features for photo quality assessment
    Yeh, Mei-Chen and Cheng, Yu-Chen (2012)
  55. The Design of High-Level Features for Photo Quality Assessment
    Yan Ke (2006)
  56. VTAMIQ: Transformers for Attention Modulated Image Quality Assessment
    Chubarau, Andrei and Clark, James (2021)
  57. Why is image quality assessment so difficult?
    Wang, Zhou and Bovik, Alan C and Lu, Ligang (2002)
  1. Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices
  2. CIFAR-10 (Canadian Institute for Advanced Research)
    Alex Krizhevsky and Vinod Nair and Geoffrey Hinton (2009)
  3. A database for perceptual evaluation of image aesthetics
    Liu, Wentao and Wang, Zhou (2017)
  4. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
    He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian (2015)
  5. IDEA: A new dataset for image aesthetic scoring
    Jin, Xin and Wu, Le and Zhao, Geng and Zhou, Xinghui and Zhang, Xiaokun and Li, Xiaodong (2020)
  6. ImageNet classification with deep convolutional neural networks
    Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E. (2017)
  7. ImageNet-21K Pretraining for the Masses
    Ridnik, Tal and Ben-Baruch, Emanuel and Noy, Asaf and Zelnik-Manor, Lihi (2021)
  8. ImageNet: A large-scale hierarchical image database
    Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Kai Li (2009)
  9. Quantitative analysis of automatic image cropping algorithms: A dataset and comparative study
    Chen, Yi Ling and Huang, Tzu Wei and Chang, Kai Han and Tsai, Yu Chen and Chen, Hwann Tzong and Chen, Bing Yu (2017)
  10. Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
    Yuan, Li and Chen, Yunpeng and Wang, Tao and Yu, Weihao and Shi, Yujun and Jiang, Zihang and Tay, Francis EH and Feng, Jiashi and Yan, Shuicheng (2021)
  1. Attention Is All You Need
  2. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
  3. Generative Art Using Neural Visual Grammars and Dual Encoders
  4. Studying aesthetics in photographic images using a computational approach
  5. Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates
    Constantin, Mihai Gabriel and Redi, Miriam and Zen, Gloria and Ionescu, Bogdan (2019)
  6. A Novel Digital Color Image Steganography using Discrete Wavelet Transform (Digital Color Image Steganography using DWT)
    Goel, A. and Deswal, V. and Chhabra, S. (2019)
  7. A robust regression method based on exponential-type kernel functions
    De Carvalho, Francisco de A.T. and Lima Neto, Eufrásio de A. and Ferreira, Marcelo R.P. (2017)
  8. A visual vocabulary for flower classification
    Nilsback, Maria Elena and Zisserman, Andrew (2006)
  9. Adam: A Method for Stochastic Optimization
    Kingma, Diederik P. and Ba, Jimmy (2014)
  10. Adaptive Attention Span in Transformers
    Sukhbaatar, Sainbayar and Grave, Edouard and Bojanowski, Piotr and Joulin, Armand (2019)
  11. Adaptive wavelet packet based audio steganography using data history
    Gu, Jinjin and Cai, Haoming Hengxing and Dong, Chao and Ren, Jimmy S. and Qiao, Yu and Gu, Shuhang and Timofte, Radu and Cheon, Manri and Yoon, Sungjun and Kang, Byungyeon and Lee, Junwoo and Zhang, Qing and Guo, Haiyang and Bin, Yi and Hou, Yuqing and Luo, Hengliang and Guo, Jingyu and Wang, Zirui and Wang, Hai and Yang, Wenming and Bai, Qingyan and Shi, Shuwei and Xia, Weihao and Cao, Mingdeng and Wang, Jiahao and Chen, Yifan and Yang, Yujiu and Li, Yang and Zhang, Tao and Feng, Longtao and Liao, Yiting and Li, Junlin and Thong, William and Pereira, Jose Costa and Leonardis, Ales and McDonagh, Steven and Xu, Kele and Yang, Lehan and Cai, Haoming Hengxing and Sun, Pengfei and Ayyoubzadeh, Seyed Mehdi and Royat, Ali and Fezza, Sid Ahmed and Hammou, Dounia and Hamidouche, Wassim and Ahn, Sewoong and Yoon, Gwangjin and Tsubota, Koki and Akutsu, Hiroaki and Aizawa, Kiyoharu and Murugan, Guru Vimal Kumar and Uthandipalayam Subramaniyam, Ragupathy and Prabakaran, G. and Bhavani, R. and Shah, Parul and Choudhari, Pranali and Sivaraman, Suresh (2020)
  12. Aesthetic Appreciation: The View From Neuroimaging
    Skov, Martin (2019)
  13. Aesthetic Critiques Generation for Photos
    Kuang-Yu Chang (2017)
  14. Aesthetic image classification for autonomous agents
    Desnoyer, Mark and Wettergreen, David (2010)
  15. Aesthetic Measure
    Birkhoff, George David (1933)
  16. Aesthetic preference for polygon shape
    Friedenberg, Jay and Bertamini, Marco (2015)
  17. Aesthetic Properties, Aesthetic Laws, and Aesthetic Principles
    Zemach, Eddy (1987)
  18. Aesthetic quality classification of photographs based on color harmony
    Nishiyama, Masashi and Okabe, Takahiro and Sato, Imari and Sato, Yoichi (2011)
  19. Aesthetics and emotions in images
    Joshi, Dhiraj and Datta, Ritendra and Fedorovskaya, Elena and Luong, Quang Tuan and Wang, James Z. and Li, Jia and Luo, Jiebo (2011)
  20. Aggregating Nested Transformers
    Zhang, Zizhao and Zhang, Han and Zhao, Long and Chen, Ting and Pfister, Tomas (2021)
  21. Algorithmic inferencing of aesthetics and emotion in natural images: An exposition
    Datta, Ritendra and Jia Li (2008)
  22. An adaptive deep Q-learning strategy for handwritten digit recognition
    Qiao, Junfei and Wang, Gongming and Li, Wenjing and Chen, Min (2018)
  23. An aesthetic perspective to explore aesthetic components of interactive system
    Wu, Yongmeng and Tan, Hao and Zhao, Jianghong (2014)
  24. An End-to-end Method for Producing Scanning-robust Stylized QR Codes
    Su, Hao and Niu, Jianwei and Liu, Xuefeng and Li, Qingfeng and Wan, Ji and Xu, Mingliang and Ren, Tao (2020)
  25. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
    Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil (2020)
  26. Artists' statements can influence perceptions of artwork
    Specht, Steven (2010)
  27. Assessing photo quality with geo-context and crowdsourced photos
    Yin, Wenyuan and Mei, Tao and Chen, Chang Wen (2012)
  28. Atlas: End-to-End 3D Scene Reconstruction from Posed Images
    Murez, Zak and van As, Tarrence and Bartolozzi, James and Sinha, Ayan and Badrinarayanan, Vijay and Rabinovich, Andrew (2020)
  29. Attention Is All You Need
    Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia (2017)
  30. Automated aesthetic analysis of photographic images
    Aydin, Tunc Ozan and Smolic, Aljoscha and Gross, Markus (2015)
  31. Automatic adaptation of object detectors to new domains using self-training
    Roychowdhury, Aruni and Chakrabarty, Prithvijit and Singh, Ashish and Jin, Souyoung and Jiang, Huaizu and Cao, Liangliang and Learned-Miller, Erik (2019)
  32. Automatic linguistic indexing of pictures by a statistical modeling approach
    Li, Jia and Wang, James Z. (2003)
  33. Automaticity and the processing of artistic photographs
    Mullennix, J. W. and Foytik, L. R. and Chan, C. H. and Dragun, B. R. and Maloney, M. and Polaski, L. (2013)
  34. Beauty in efficiency: An experimental enquiry into the principle of maximum effect for minimum means
    Da Silva, Odette and Crilly, Nathan and Hekkert, Paul (2017)
  35. Big Transfer (BiT): General Visual Representation Learning
    Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Puigcerver, Joan and Yung, Jessica and Gelly, Sylvain and Houlsby, Neil (2020)
  36. Bottleneck Transformers for Visual Recognition
    Srinivas, Aravind and Lin, Tsung-Yi and Parmar, Niki and Shlens, Jonathon and Abbeel, Pieter and Vaswani, Ashish (2021)
  37. Bottleneck Transformers for Visual Recognition
    Srinivas, Aravind and Lin, Tsung-Yi and Parmar, Niki and Shlens, Jonathon and Abbeel, Pieter and Vaswani, Ashish (2021)
  38. Classification of digital photos taken by photographers or home users
    Tong, Hanghang and Li, Mingjing and Zhang, Hong Jiang and He, Jingrui and Zhang, Changshui (2004)
  39. CoAtNet: Marrying Convolution and Attention for All Data Sizes
    Dai, Zihang and Liu, Hanxiao and Le, Quoc V. and Tan, Mingxing (2021)
  40. Complexity scale and aesthetic judgments of color combinations
    Tsutsui, Ako and Ohmi, Gentarow (2011)
  41. Computational beauty: Aesthetic judgment at the intersection of art and science
    Spratt, Emily L. and Elgammal, Ahmed (2015)
  42. Computer Models for Facial Beauty Analysis
    Zhang, David and Chen, Fangmei and Xu, Yong (2016)
  43. Craquelurenet: Matching The Crack Structure In Historical Paintings For Multi-Modal Image Registration
    Sindel, Aline and Maier, Andreas and Christlein, Vincent (2021)
  44. Deep Decision Network for Multi-class Image Classification
    Murthy, Venkatesh N and Singh, Vivek and Chen, Terrence and Manmatha, R and Comaniciu, Dorin (2016)
  45. Deep image aesthetics classification using inception modules and fine-tuning connected layer
    Jin, Xin and Chi, Jingying and Peng, Siwei and Tian, Yulu and Ye, Chaochen and Li, Xiaodong (2016)
  46. Deep Layer Aggregation
    Yu, Fisher and Wang, Dequan and Shelhamer, Evan and Darrell, Trevor (2017)
  47. Deep reinforcement active learning for human-in-the-loop person re-identification
    Liu, Zimo and Wang, Jingya and Gong, Shaogang and Tao, Dacheng and Lu, Huchuan (2019)
  48. Deep Residual Learning for Image Recognition
    He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian (2016)
  49. Deep Residual Learning for Image Recognition
    He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian (2015)
  50. Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion
    Sun, Peng and Zhang, Wenhu and Wang, Huanyu and Li, Songyuan and Li, Xi (2021)
  51. Densely connected convolutional networks
    Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q. (2017)
  52. Disentangled Representation Learning of Makeup Portraits in the Wild
    Li, Yi and Huang, Huaibo and Cao, Jie and He, Ran and Tan, Tieniu (2020)
  53. Early Convolutions Help Transformers See Better
    Xiao, Tete and Singh, Mannat and Mintun, Eric and Darrell, Trevor and Doll\'a (2021)
  54. Effect of facial makeup style recommendation on visual sensibility
    Chung, Kyung-Yong (2014)
  55. Effective aesthetics prediction with multi-level spatially pooled features
    Hosu, Vlad and Goldlucke, Bastian and Saupe, Dietmar (2019)
  56. Effects of Preceding Context on Aesthetic Preference
    Mullennix, John W. and Kristo, Grant M. and Robinet, Julien (2020)
  57. Efficient Transformers: A Survey
    Tay, Yi and Dehghani, Mostafa and Bahri, Dara and Metzler, Donald (2020)
  58. Emotional sentiment analysis for a group of people based on transfer learning with a multi-modal system
    Bawa, Vivek Singh and Kumar, Vinay (2019)
  59. End-to-end Chinese landscape painting creation using generative adversarial networks
    Xue, Alice (2021)
  60. Engineering Deep Representations for Modeling Aesthetic Perception.
    Chen, Yanxiang and Hu, Yuxing and Zhang, Luming and Li, Ping and Zhang, Chao (2018)
  61. Evolution of entropy in art painting based on the wavelet transform
    Yang, Hongyi and Yang, Han (2021)
  62. Exploring principles-of-art features for image emotion recognition
    Zhao, Sicheng and Gao, Yue and Jiang, Xiaolei and Yao, Hongxun and Chua, Tat Seng and Sun, Xiaoshuai (2014)
  63. Eye tracking for understanding aesthetic of ambiguity
    Younes, Elhem and Bardakos, John and Lioret, Alain (2016)
  64. Face and Facial Expression Recognition using Three Dimensional Data
    Mpiperis, Iordanis and Malassiotis, Sotiris and Strintzis, Michael G. (2008)
  65. Feature Comparison Based Channel Attention For Fine-Grained Visual Classification
    Jia, Shukun and Bai, Yan and Zhang, Jing (2020)
  66. Feeling Beauty
    Starr, G. Gabrielle (2014)
  67. Fisher Kernels on Visual Vocabularies for Image Categorization
    Perronnin, Florent and Dance, Christopher (2007)
  68. Fully Convolutional Networks for Semantic Segmentation
    Shelhamer, Evan and Long, Jonathan and Darrell, Trevor (2017)
  69. Fusion of multichannel local and global structural cues for photo aesthetics evaluation
    Zhang, Luming and Gao, Yue and Zimmermann, Roger and Tian, Qi and Li, Xuelong (2014)
  70. Gaussian Error Linear Units (GELUs)
    Hendrycks, Dan and Gimpel, Kevin (2016)
  71. Generative Adversarial Networks
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  72. Generative Art Using Neural Visual Grammars and Dual Encoders
    Fernando, Chrisantha and Eslami, S M Ali and Alayrac, Jean-baptiste and Mirowski, Piotr and Banarse, Dylan and Osindero, Simon (2021)
  73. Geometric Regularity, Symmetry and the Perceived Beauty of Simple Shapes
    Friedenberg, Jay (2018)
  74. Going deeper with convolutions
    Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew (2015)
  75. Going deeper with Image Transformers
    Touvron, Hugo and Cord, Matthieu and Sablayrolles, Alexandre and Synnaeve, Gabriel and J\'e (2021)
  76. Gradient-based learning applied to document recognition
    Lecun, Y. and Bottou, Léon and Bengio, Yoshua and Haffner, Patrick (1998)
  77. High level describable attributes for predicting aesthetics and interestingness
    Dhar, Sagnik and Ordonez, Vicente and Berg, Tamara L (2011)
  78. Holographic Reduced Representations: Convolutional for Compositional Distributed Representations
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  79. How to find refutations of the golden section without really trying
    Green, Christopher (2012)
  80. Humans prefer curved visual objects
    Bar, Moshe and Neta, Maital (2006)
  81. Illumination Normalization for Color Face Images
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  82. Image aesthetics depends on context
    Simond, Florian and Arvanitopoulos, Nikolaos and S\"u (2015)
  83. Image Analysis and Processing – ICIAP 2013
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  84. Image and Signal Processing
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  85. Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency
    Yee, Kyra and Tantipongpipat, Uthaipon and Mishra, Shubhanshu (2021)
  86. Image processing for the analysis and conservation of paintings: Opportunities and challenges
    Barni, Mauro and Pelagotti, Anna and Piva, Alessandro (2005)
  87. Implicit association of symmetry with positive valence, high arousal and simplicity
    Bertamini, Marco and Makin, Alexis and Rampone, Giulia (2013)
  88. Internal Emotion Classification Using EEG Signal with Sparse Discriminative Ensemble
    Ullah, Habib and Uzair, Muhammad and Mahmood, Arif and Ullah, Mohib and Khan, Sultan Daud and Cheikh, Faouzi Alaya (2019)
  89. Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
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  90. Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature
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  91. Learning Cascade Attention for fine-grained image classification
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  92. Learning the change for automatic image cropping
    Yan, Jianzhou and Lin, Stephen and Kang, Sing Bing and Tang, Xiaoou (2013)
  93. Learning to Compose with Professional Photographs on the Web
    Chen, Yi-Ling and Klopp, Jan and Sun, Min and Chien, Shao-Yi and Ma, Kwan-Liu (2017)
  94. Learning to photograph
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  95. Learning to Photograph: A Compositional Perspective
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  96. Learning to predict the perceived visual quality of photos
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  98. Long Short-Term Memory
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  100. Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
    Ghadiyaram, Deepti and Bovik, Alan C. (2015)
  101. MLP-Mixer: An all-MLP Architecture for Vision
    Tolstikhin, Ilya and Houlsby, Neil and Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Unterthiner, Thomas and Yung, Jessica and Steiner, Andreas and Keysers, Daniel and Uszkoreit, Jakob and Lucic, Mario and Dosovitskiy, Alexey (2021)
  102. MSCAN: Multimodal Self-and-Collaborative Attention Network for image aesthetic prediction tasks
    Zhang, Xiaodan and Gao, Xinbo and He, Lihuo and Lu, Wen (2021)
  103. Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker
    Jiang, Mingxin and Hai, Tao and Pan, Zhigeng and Wang, Haiyan and Jia, Yinjie and Deng, Chao (2019)
  104. Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
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