We train a deep Convolutional Neural Network (CNN) from scratch for visual aesthetic analysis in images and discuss techniques we adopt to improve the accuracy. We avoid the prevalent best transfer learning approaches of using pretrained weights to perform the task and train a model from scratch to get accuracy of 78.7% on AVA2 Dataset close to the best models available (85.6%). We further show that accuracy increases to 81.48% on increasing the training set by incremental 10 percentile of entire AVA dataset showing our algorithm gets better with more data.
Beijing Electronic Science and Technology Institute（北京电子科技学院）；University of Science and Technology of China（中国科学技术大学）；University of California, Davis（加州大学戴维斯分校）
Aesthetic assessment of images can be categorized into two main forms: numerical assessment and language assessment. Aesthetics caption of photographs is the only task of aesthetic language assessment that has been addressed. In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images. If we give a question of images aesthetics, model can predict the answer. We use images from \textit{www.flickr.com}. The objective QA pairs are generated by the proposed aesthetic attributes analysis algorithms. Moreover, we introduce subjectiv…
Recent studies have shown remarkable success in universal style transfer which transfers arbitrary visual styles to content images. However, existing approaches suffer from the aesthetic-unrealistic problem that introduces disharmonious patterns and evident artifacts, making the results easy to spot from real paintings. To address this limitation, we propose AesUST, a novel Aesthetic-enhanced Universal Style Transfer approach that can generate aesthetically more realistic and pleasing results for arbitrary styles. Specifically, our approach introduces an aesthetic discriminator to learn the un…
Learning to Look before Learning to Like: Incorporating Human Visual Cognition into Aesthetic Quality Assessment
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Zhongshan Ophthalmic Center, Sun Yat-sen University（中山眼科中心，中山大学）；School of computer science, Torrens University Australia（计算机科学学院，托伦斯大学澳大利亚分校）
School of Information Science and Engineering, Xiamen University（厦门大学信息科学与技术学院）；Institute of Information Science, Academia Sinica（中央研究院信息科学研究所）
Despite recent progress, computational visual aesthetic is still challenging. Image cropping, which refers to the removal of unwanted scene areas, is an important step to improve the aesthetic quality of an image. However, it is challenging to evaluate whether cropping leads to aesthetically pleasing results because the assessment is typically subjective. In this paper, we propose a novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers. The proposed CCR method improves the convergence speed of the cascaded method, wh…
Beautifeye；IADT（爱尔兰艺术设计与技术学院）；Xerox Research Centre Europe（施乐欧洲研究中心）
Aesthetic image analysis is the study and assessment of the aesthetic properties of images. Current computational approaches to aesthetic image analysis either provide accurate or interpretable results. To obtain both accuracy and interpretability by humans, we advocate the use of learned and nameable visual attributes as mid-level features. For this purpose, we propose to discover and learn the visual appearance of attributes automatically, using a recently introduced database, called AVA, which contains more than 250,000 images together with their aesthetic scores and textual comments given …
AesRM: Improving Video Aesthetics with Expert-Level Feedback
The University of Hong Kong（香港大学）；Fudan University（复旦大学）；Zhejiang University（浙江大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；University of Sydney（悉尼大学）；Alibaba Group（阿里巴巴集团）
Rating how aesthetically pleasing an image appears is a highly complex matter and depends on a large number of different visual factors. Previous work has tackled the aesthetic rating problem by ranking on a 1-dimensional rating scale, e.g., incorporating handcrafted attributes. In this paper, we propose a rather general approach to automatically map aesthetic pleasingness with all its complexity into an "aesthetic space" to allow for a highly fine-grained resolution. In detail, making use of deep learning, our method directly learns an encoding of a given image into this high-dimensional feat…
Tsinghua University（清华大学）；Nanjing University（南京大学）；National University of Singapore (Suzhou) Research Institute（新加坡国立大学（苏州）研究院）
Many aesthetic models in computer vision suffer from two shortcomings: 1) the low descriptiveness and interpretability of those hand-crafted aesthetic criteria (i.e., nonindicative of region-level aesthetics), and 2) the difficulty of engineering aesthetic features adaptively and automatically toward different image sets. To remedy these problems, we develop a deep architecture to learn aesthetically-relevant visual attributes from Flickr1, which are localized by multiple textual attributes in a weakly-supervised setting. More specifically, using a bag-ofwords (BoW) representation of the frequ…
Tallinn University（塔林大学）；University of Cambridge（剑桥大学）；The Alan Turing Institute（阿兰·图灵研究所）
The quantification of visual aesthetics and complexity have a long history, the latter previously operationalized via the application of compression algorithms. Here we generalize and extend the compression approach beyond simple complexity measures to quantify algorithmic distance in historical and contemporary visual media. The proposed "ensemble" approach works by compressing a large number of transformed versions of a given input image, resulting in a vector of associated compression ratios. This approach is more efficient than other compression-based algorithmic distances, and is particul…
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；ProjectHAT
We explore computational approaches for visual guidance to aid in creating aesthetically pleasing art and graphic design. Our work complements and builds on previous work that developed models for how humans look at images. Our approach comprises three steps. First, we collected a dataset of art masterpieces and labeled the visual fixations with state-of-art vision models. Second, we clustered the visual guidance templates of the art masterpieces with unsupervised learning. Third, we developed a pipeline using generative adversarial networks to learn the principles of visual guidance and that …
Beijing Electronic Science and Technology Institute（北京电子科技学院）；CETC Big Data Research Institute Co.,Ltd.（中电科大数据研究院有限公司）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；SenseTime Research（商汤科技研究院）；Beihang University（北京航空航天大学）；Peng Cheng Laboratory（鹏城实验室）
Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named \emph{DPC-Captions} which contains comments of up to 5 aest…
Trinity College Dublin（都柏林圣三一学院）；Accenture Labs（埃森哲实验室）
Aspect ratio and spatial layout are two of the principal factors determining the aesthetic value of a photograph. But, incorporating these into the traditional convolution-based frameworks for the task of image aesthetics assessment is problematic. The aspect ratio of the photographs gets distorted while they are resized/cropped to a fixed dimension to facilitate training batch sampling. On the other hand, the convolutional filters process information locally and are limited in their ability to model the global spatial layout of a photograph. In this work, we present a two-stage framework base…
Image aesthetics has become an important criterion for visual content curation on social media sites and media content repositories. Previous work on aesthetic prediction models in the computer vision community has focused on aesthetic score prediction or binary image labeling. However, raw aesthetic annotations are in the form of score histograms and provide richer and more precise information than binary labels or mean scores. Consequently, in this work we focus on the rarely-studied problem of predicting aesthetic score distributions and propose a novel architecture and training procedure f…
Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the la…
APDDv2: Aesthetics of Paintings and Drawings Dataset with Artist Labeled Scores and Comments
Beijing Electronic Science and Technology Institute（北京电子科技学院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Central Academy of Fine Arts（中央美术学院）；University of Science and Technology of China（中国科学技术大学）；Beijing University of Technology（北京工业大学）
We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new content, for one-shot transfer of the source style to new content. We learn this disentanglement in a self-supervised manner. Our method processes entire word boxes, without requiring segmentation of text from background, per-character processing, or making assumptions on string lengths. We show results in different text domains which were previously handled by specialized methods, e.g., scene text, handwritten text. To…
UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Peking University（北京大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Nanjing University（南京大学）；Sun Yat-sen University（中山大学）；Tele-AI Website（Tele-AI网站）
With the increased sophistication of AI techniques, the application of these systems has been expanding to ever newer fields. Increasingly, these systems are being used in modeling of human aesthetics and creativity, e.g. how humans create artworks and design products. Our lab has developed one such AI creativity deep learning system that can be used to create artworks in the form of images and videos. In this paper, we describe this system and its use in studying the human visual system and the formation of aesthetic experiences. Specifically, we show how time-based AI created media can be us…
This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality photos from low-quality ones based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature trying to solve this challenging problem. In this survey, we present a systematic listing of the reviewed approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (dataset…
Institute for Datability Science（数据可持续性科学研究所）；Osaka University（大阪大学）
In computer vision, visual arts are often studied from a purely aesthetics perspective, mostly by analysing the visual appearance of an artistic reproduction to infer its style, its author, or its representative features. In this work, however, we explore art from both a visual and a language perspective. Our aim is to bridge the gap between the visual appearance of an artwork and its underlying meaning, by jointly analysing its aesthetics and its semantics. We introduce the use of multi-modal techniques in the field of automatic art analysis by 1) collecting a multi-modal dataset with fine-ar…
Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, we formulate a novel problem of ra…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Youtu Lab, Tencent（腾讯优图实验室）；Snap Inc.（Snap公司）；North China Electric Power University（华北电力大学）；Xiamen University（厦门大学）；Kuaishou Technology（快手科技）
Visual aesthetic assessment has been an active research field for decades. Although latest methods have achieved promising performance on benchmark datasets, they typically rely on a large number of manual annotations including both aesthetic labels and related image attributes. In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective. Our motivation is that a suitable feature representation for image aesthetic assessment should be able to distinguish different expert-designed image manipulations, which have close relationships w…
University of Science and Technology of China（中国科学技术大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；University of California, Davis（加州大学戴维斯分校）
Image aesthetic quality assessment is popular during the last decade. Besides numerical assessment, nature language assessment (aesthetic captioning) has been proposed to describe the generally aesthetic impression of an image. In this paper, we propose aesthetic attribute assessment, which is the aesthetic attributes captioning, i.e., to assess the aesthetic attributes such as composition, lighting usage and color arrangement. It is a non-trivial task to label the comments of aesthetic attributes, which limit the scale of the corresponding datasets. We construct a novel dataset, named DPC-Cap…
A toolbox for calculating objective image properties in aesthetics research
University Hospital Jena（耶拿大学医院）；University of Leuven (KU Leuven)（鲁汶大学（KU Leuven））；University of New South Wales（新南威尔士大学）；University of Konstanz（康斯坦茨大学）
Nowadays, social media has become a popular platform for the public to share photos. To make photos more visually appealing, users usually apply filters on their photos without domain knowledge. However, due to the growing number of filter types, it becomes a major issue for users to choose the best filter type. For this purpose, filter recommendation for photo aesthetics takes an important role in image quality ranking problems. In these years, several works have declared that Convolutional Neural Networks (CNNs) outperform traditional methods in image aesthetic categorization, which classifi…
Enhancing Zero-shot Personalized Image Aesthetics Assessment with Profile-aware Multimodal LLM
Mashang Consumer Finance Co., Ltd.（马莎消费金融有限公司）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics
Harvard University（哈佛大学）；Harvard School of Public Health（哈佛公共卫生学院）；Massachusetts General Hospital（麻省总医院）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Media Technology Center (MTC)（媒体技术中心（MTC））；ETH Zurich（苏黎世联邦理工学院）；DISCo；University of Milano-Bicocca（米兰比可卡大学）
Computational inference of aesthetics is an ill-defined task due to its subjective nature. Many datasets have been proposed to tackle the problem by providing pairs of images and aesthetic scores based on human ratings. However, humans are better at expressing their opinion, taste, and emotions by means of language rather than summarizing them in a single number. In fact, photo critiques provide much richer information as they reveal how and why users rate the aesthetics of visual stimuli. In this regard, we propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and p…
We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability, aesthetics, and emotional valence. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more or less memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become mor…
International Institute of Information Technology（国际信息技术学院）；University of Hyderabad（海得拉巴大学）
Automatic photo aesthetic assessment is a challenging artificial intelligence task. Existing computational approaches have focused on modeling a single aesthetic score or a class (good or bad), however these do not provide any details on why the photograph is good or bad, or which attributes contribute to the quality of the photograph. To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score. For this purpose, we propose a novel multitask deep convolution neural network, which jointly learns eigh…
Learning fine-grained details is a key issue in image aesthetic assessment. Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information. Extensive studies show that humans perceive fine-grained details with a mixture of foveal vision and peripheral vision. Fovea has the highest possible visual acuity and is responsible for seeing the details. The peripheral vision is used for perceiving the broad spatial scene and selecting the attended regions for the fovea. Inspired by these observations, we propose a G…
Visual Aesthetic Benchmark: Can Frontier Models Judge Beauty?
Bake AI；University of Washington（华盛顿大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Stanford University（斯坦福大学）；University of Notre Dame（诺丁汉大学）；Carnegie Mellon University（卡内基梅隆大学）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；Western Washington University（西雅图华盛顿大学）；King Abdulaziz City for Science and Technology（国王阿卜杜勒阿齐兹科技城）
