{"results":[{"id":"ss_7e34d15b1e13459a5f14b75c85c4c2c8932b9b84","title":"Reflexive Modernization: Politics, Tradition and Aesthetics in the Modern Social Order","authors":[{"name":"U. Beck"},{"name":"A. Giddens"},{"name":"S. Lash"}],"abstract":"","source":"Semantic Scholar","year":1994,"language":"en","subjects":["Sociology","Economics"],"doi":"10.2307/3341775","url":"https://www.semanticscholar.org/paper/7e34d15b1e13459a5f14b75c85c4c2c8932b9b84","is_open_access":true,"citations":3315,"published_at":"","score":80},{"id":"ss_2213c5eee0288c61a2521dc12d8ebdeeaeb1fa48","title":"Aesthetics and Psychobiology","authors":[{"name":"George Gunkle"},{"name":"D. Berlyne"}],"abstract":"","source":"Semantic Scholar","year":1975,"language":"en","subjects":["Psychology"],"doi":"10.2307/3206370","url":"https://www.semanticscholar.org/paper/2213c5eee0288c61a2521dc12d8ebdeeaeb1fa48","is_open_access":true,"citations":2326,"published_at":"","score":80},{"id":"ss_4c4db93ea130ba18bd2c53de2b22e213c6823ec8","title":"Photo Aesthetics Ranking Network with Attributes and Content Adaptation","authors":[{"name":"Shu Kong"},{"name":"Xiaohui Shen"},{"name":"Zhe L. Lin"},{"name":"R. Měch"},{"name":"Charless C. Fowlkes"}],"abstract":"Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high- or low-aesthetic categories. In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated photo aesthetics rating problem.","source":"Semantic Scholar","year":2016,"language":"en","subjects":["Computer Science","Mathematics"],"doi":"10.1007/978-3-319-46448-0_40","url":"https://www.semanticscholar.org/paper/4c4db93ea130ba18bd2c53de2b22e213c6823ec8","is_open_access":true,"citations":506,"published_at":"","score":75.18},{"id":"ss_9700840765ec29dfa3b05246a2a40ad198af81ee","title":"Anthocyanin Pigments: Beyond Aesthetics","authors":[{"name":"Bindhu Alappat"},{"name":"Jayaraj Alappat"}],"abstract":"Anthocyanins are polyphenol compounds that render various hues of pink, red, purple, and blue in flowers, vegetables, and fruits. Anthocyanins also play significant roles in plant propagation, ecophysiology, and plant defense mechanisms. Structurally, anthocyanins are anthocyanidins modified by sugars and acyl acids. Anthocyanin colors are susceptible to pH, light, temperatures, and metal ions. The stability of anthocyanins is controlled by various factors, including inter and intramolecular complexations. Chromatographic and spectrometric methods have been extensively used for the extraction, isolation, and identification of anthocyanins. Anthocyanins play a major role in the pharmaceutical; nutraceutical; and food coloring, flavoring, and preserving industries. Research in these areas has not satisfied the urge for natural and sustainable colors and supplemental products. The lability of anthocyanins under various formulated conditions is the primary reason for this delay. New gene editing technologies to modify anthocyanin structures in vivo and the structural modification of anthocyanin via semi-synthetic methods offer new opportunities in this area. This review focusses on the biogenetics of anthocyanins; their colors, structural modifications, and stability; their various applications in human health and welfare; and advances in the field.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Chemistry","Medicine"],"doi":"10.3390/molecules25235500","url":"https://www.semanticscholar.org/paper/9700840765ec29dfa3b05246a2a40ad198af81ee","pdf_url":"https://www.mdpi.com/1420-3049/25/23/5500/pdf","is_open_access":true,"citations":312,"published_at":"","score":73.36},{"id":"ss_b3bc3238dace58739b32a17cc55ba1428c0a62a0","title":"Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound","authors":[{"name":"Andros Tjandra"},{"name":"Yi-Chiao Wu"},{"name":"Baishan Guo"},{"name":"John Hoffman"},{"name":"Brian Ellis"},{"name":"Apoorv Vyas"},{"name":"Bowen Shi"},{"name":"Sanyuan Chen"},{"name":"Matt Le"},{"name":"N. Zacharov"},{"name":"Carleigh Wood"},{"name":"Ann Lee"},{"name":"Wei-Ning Hsu"}],"abstract":"The quantification of audio aesthetics remains a complex challenge in audio processing, primarily due to its subjective nature, which is influenced by human perception and cultural context. Traditional methods often depend on human listeners for evaluation, leading to inconsistencies and high resource demands. This paper addresses the growing need for automated systems capable of predicting audio aesthetics without human intervention. Such systems are crucial for applications like data filtering, pseudo-labeling large datasets, and evaluating generative audio models, especially as these models become more sophisticated. In this work, we introduce a novel approach to audio aesthetic evaluation by proposing new annotation guidelines that decompose human listening perspectives into four distinct axes. We develop and train no-reference, per-item prediction models that offer a more nuanced assessment of audio quality. Our models are evaluated against human mean opinion scores (MOS) and existing methods, demonstrating comparable or superior performance. This research not only advances the field of audio aesthetics but also provides open-source models and datasets to facilitate future work and benchmarking. We release our code and pre-trained model at: https://github.com/facebookresearch/audiobox-aesthetics","source":"Semantic Scholar","year":2025,"language":"en","subjects":["Computer Science","Engineering"],"doi":"10.48550/arXiv.2502.05139","url":"https://www.semanticscholar.org/paper/b3bc3238dace58739b32a17cc55ba1428c0a62a0","is_open_access":true,"citations":121,"published_at":"","score":72.63},{"id":"ss_55ba76bdca99a1ab07af91e0ebde0bf595d71652","title":"A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping","authors":[{"name":"Wenguan Wang"},{"name":"Jianbing Shen"},{"name":"Haibin Ling"}],"abstract":"We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Computer Science","Medicine"],"doi":"10.1109/TPAMI.2018.2840724","url":"https://www.semanticscholar.org/paper/55ba76bdca99a1ab07af91e0ebde0bf595d71652","is_open_access":true,"citations":316,"published_at":"","score":72.48},{"id":"ss_558afbf12b263a98a474a1d2d1ace6add7bda091","title":"Aesthetics","authors":[{"name":"Alessandro Bertinetto"}],"abstract":"","source":"Semantic Scholar","year":2023,"language":"en","subjects":null,"doi":"10.3138/9781442694378-003","url":"https://www.semanticscholar.org/paper/558afbf12b263a98a474a1d2d1ace6add7bda091","is_open_access":true,"citations":109,"published_at":"","score":70.27000000000001},{"id":"ss_b343de80301ee2716b3175c895fa881fb5a811a6","title":"Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks","authors":[{"name":"Shuaiqi He"},{"name":"Yongchang Zhang"},{"name":"Rui Xie"},{"name":"Dongxiang Jiang"},{"name":"Anlong Ming"}],"abstract":"Challenges in image aesthetics assessment (IAA) arise from that images of different themes correspond to different evaluation criteria, and learning aesthetics directly from images while ignoring the impact of theme variations on human visual perception inhibits the further development of IAA; however, existing IAA datasets and models overlook this problem. To address this issue, we show that a theme-oriented dataset and model design are effective for IAA. Specifically, 1) we elaborately build a novel dataset, called TAD66K, that contains 66K images covering 47 popular themes, and each image is densely annotated by more than 1200 people with dedicated theme evaluation criteria. 2) We develop a baseline model, TANet, which can effectively extract theme information and adaptively establish perception rules to evaluate images with different themes. 3) We develop a large-scale benchmark (the most comprehensive thus far) by comparing 17 methods with TANet on three representative datasets: AVA, FLICKR-AES and the proposed TAD66K, TANet achieves state-of-the-art performance on all three datasets. Our work offers the community an opportunity to explore more challenging directions; the code, dataset and supplementary material are available at https://github.com/woshidandan/TANet.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Computer Science"],"doi":"10.24963/ijcai.2022/132","url":"https://www.semanticscholar.org/paper/b343de80301ee2716b3175c895fa881fb5a811a6","pdf_url":"https://www.ijcai.org/proceedings/2022/0132.pdf","is_open_access":true,"citations":138,"published_at":"","score":70.14},{"id":"arxiv_2602.08363","title":"Roadmap to Quantum Aesthetics","authors":[{"name":"Ivan C. H. Liu"},{"name":"Hsiao-Yuan Chen"}],"abstract":"Quantum mechanics occupies a central position in contemporary science while remaining largely inaccessible to direct sensory experience. This paper proposes a roadmap to quantum aesthetics that examines how quantum concepts become aesthetic phenomena through artistic mediation rather than direct representation. Two complementary and orthogonal approaches are articulated. The first, a pioneering top-down approach, employs text-prompt-based generative AI to probe quantum aesthetics as a collective cultural construct embedded in large-scale training data. By systematically modulating the linguistic weight of the term \"quantum,\" generative models are used as experimental environments to reveal how quantum imaginaries circulate within contemporary visual culture. The second, a bottom-up approach, derives aesthetic form directly from quantum-mechanical structures through the visualization of quantum-generated data, exemplified here by hydrogen atomic orbitals calculated from the Schrödinger equation. These approaches are framed not as competing methods but as intersecting paths within a navigable field of artistic research. They position quantum aesthetics as an emergent field of artistic research shaped by cultural imagination, computational mediation, and physical law, opening new directions for artistic practice and pedagogy at the intersection of art, data, artificial intelligence and quantum science.","source":"arXiv","year":2026,"language":"en","subjects":["physics.pop-ph","cs.AI","quant-ph"],"url":"https://arxiv.org/abs/2602.08363","pdf_url":"https://arxiv.org/pdf/2602.08363","is_open_access":true,"published_at":"2026-02-09T08:00:09Z","score":70},{"id":"ss_cd87d7e10d7d767f3709a2d880bbf3d6d581fefb","title":"Theme-Aware Visual Attribute Reasoning for Image Aesthetics Assessment","authors":[{"name":"Leida Li"},{"name":"Yipo Huang"},{"name":"Jinjian Wu"},{"name":"Yuzhe Yang"},{"name":"Yaqian Li"},{"name":"Yandong Guo"},{"name":"Guangming Shi"}],"abstract":"People usually assess image aesthetics according to visual attributes, e.g., interesting content, good lighting and vivid color, etc. Further, the perception of visual attributes depends on the image theme. Therefore, the inherent relationship between visual attributes and image theme is crucial for image aesthetics assessment (IAA), which has not been comprehensively investigated. With this motivation, this paper presents a new IAA model based on Theme-Aware Visual Attribute Reasoning (TAVAR). The underlying idea is to simulate the process of human perception in image aesthetics by performing bilevel reasoning. Specifically, a visual attribute analysis network and a theme understanding network are first pre-trained to extract aesthetic attribute features and theme features, respectively. Then, the first level Attribute-Theme Graph (ATG) is built to investigate the coupling relationship between visual attributes and image theme. Further, a flexible aesthetics network is introduced to extract general aesthetic features, based on which we built the second level Attribute-Aesthetics Graph (AAG) to mine the relationship between theme-aware visual attributes and aesthetic features, producing the final aesthetic prediction. Extensive experiments on four public IAA databases demonstrate the superiority of the proposed TAVAR model over the state-of-the-arts. Furthermore, TAVAR features better explainability due to the use of visual attributes.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1109/TCSVT.2023.3249185","url":"https://www.semanticscholar.org/paper/cd87d7e10d7d767f3709a2d880bbf3d6d581fefb","is_open_access":true,"citations":76,"published_at":"","score":69.28},{"id":"arxiv_2502.05139","title":"Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound","authors":[{"name":"Andros Tjandra"},{"name":"Yi-Chiao Wu"},{"name":"Baishan Guo"},{"name":"John Hoffman"},{"name":"Brian Ellis"},{"name":"Apoorv Vyas"},{"name":"Bowen Shi"},{"name":"Sanyuan Chen"},{"name":"Matt Le"},{"name":"Nick Zacharov"},{"name":"Carleigh Wood"},{"name":"Ann Lee"},{"name":"Wei-Ning Hsu"}],"abstract":"The quantification of audio aesthetics remains a complex challenge in audio processing, primarily due to its subjective nature, which is influenced by human perception and cultural context. Traditional methods often depend on human listeners for evaluation, leading to inconsistencies and high resource demands. This paper addresses the growing need for automated systems capable of predicting audio aesthetics without human intervention. Such systems are crucial for applications like data filtering, pseudo-labeling large datasets, and evaluating generative audio models, especially as these models become more sophisticated. In this work, we introduce a novel approach to audio aesthetic evaluation by proposing new annotation guidelines that decompose human listening perspectives into four distinct axes. We develop and train no-reference, per-item prediction models that offer a more nuanced assessment of audio quality. Our models are evaluated against human mean opinion scores (MOS) and existing methods, demonstrating comparable or superior performance. This research not only advances the field of audio aesthetics but also provides open-source models and datasets to facilitate future work and benchmarking. We release our code and pre-trained model at: https://github.com/facebookresearch/audiobox-aesthetics","source":"arXiv","year":2025,"language":"en","subjects":["cs.SD","cs.LG","eess.AS"],"url":"https://arxiv.org/abs/2502.05139","pdf_url":"https://arxiv.org/pdf/2502.05139","is_open_access":true,"published_at":"2025-02-07T18:15:57Z","score":69},{"id":"arxiv_2505.05331","title":"Aesthetics Without Semantics","authors":[{"name":"C. Alejandro Parraga"},{"name":"Olivier Penacchio"},{"name":"Marcos Muňoz Gonzalez"},{"name":"Bogdan Raducanu"},{"name":"Xavier Otazu"}],"abstract":"While it is easy for human observers to judge an image as beautiful or ugly, aesthetic decisions result from a combination of entangled perceptual and cognitive (semantic) factors, making the understanding of aesthetic judgements particularly challenging from a scientific point of view. Furthermore, our research shows a prevailing bias in current databases, which include mostly beautiful images, further complicating the study and prediction of aesthetic responses. We address these limitations by creating a database of images with minimal semantic content and devising, and next exploiting, a method to generate images on the ugly side of aesthetic valuations. The resulting Minimum Semantic Content (MSC) database consists of a large and balanced collection of 10,426 images, each evaluated by 100 observers. We next use established image metrics to demonstrate how augmenting an image set biased towards beautiful images with ugly images can modify, or even invert, an observed relationship between image features and aesthetics valuation. Taken together, our study reveals that works in empirical aesthetics attempting to link image content and aesthetic judgements may magnify, underestimate, or simply miss interesting effects due to a limitation of the range of aesthetic values they consider.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV","q-bio.NC","stat.CO"],"url":"https://arxiv.org/abs/2505.05331","pdf_url":"https://arxiv.org/pdf/2505.05331","is_open_access":true,"published_at":"2025-05-08T15:22:11Z","score":69},{"id":"arxiv_2509.04378","title":"Aesthetic Image Captioning with Saliency Enhanced MLLMs","authors":[{"name":"Yilin Tao"},{"name":"Jiashui Huang"},{"name":"Huaze Xu"},{"name":"Ling Shao"}],"abstract":"Aesthetic Image Captioning (AIC) aims to generate textual descriptions of image aesthetics, becoming a key research direction in the field of computational aesthetics. In recent years, pretrained Multimodal Large Language Models (MLLMs) have advanced rapidly, leading to a significant increase in image aesthetics research that integrates both visual and textual modalities. However, most existing studies on image aesthetics primarily focus on predicting aesthetic ratings and have shown limited application in AIC. Existing AIC works leveraging MLLMs predominantly rely on fine-tuning methods without specifically adapting MLLMs to focus on target aesthetic content. To address this limitation, we propose the Aesthetic Saliency Enhanced Multimodal Large Language Model (ASE-MLLM), an end-to-end framework that explicitly incorporates aesthetic saliency into MLLMs. Within this framework, we introduce the Image Aesthetic Saliency Module (IASM), which efficiently and effectively extracts aesthetic saliency features from images. Additionally, we design IAS-ViT as the image encoder for MLLMs, this module fuses aesthetic saliency features with original image features via a cross-attention mechanism. To the best of our knowledge, ASE-MLLM is the first framework to integrate image aesthetic saliency into MLLMs specifically for AIC tasks. Extensive experiments demonstrated that our approach significantly outperformed traditional methods and generic MLLMs on current mainstream AIC benchmarks, achieving state-of-the-art (SOTA) performance.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2509.04378","pdf_url":"https://arxiv.org/pdf/2509.04378","is_open_access":true,"published_at":"2025-09-04T16:40:15Z","score":69},{"id":"arxiv_2510.23272","title":"Code Aesthetics with Agentic Reward Feedback","authors":[{"name":"Bang Xiao"},{"name":"Lingjie Jiang"},{"name":"Shaohan Huang"},{"name":"Tengchao Lv"},{"name":"Yupan Huang"},{"name":"Xun Wu"},{"name":"Lei Cui"},{"name":"Furu Wei"}],"abstract":"Large Language Models (LLMs) have become valuable assistants for developers in code-related tasks. While LLMs excel at traditional programming tasks such as code generation and bug fixing, they struggle with visually-oriented coding tasks, often producing suboptimal aesthetics. In this paper, we introduce a new pipeline to enhance the aesthetic quality of LLM-generated code. We first construct AesCode-358K, a large-scale instruction-tuning dataset focused on code aesthetics. Next, we propose agentic reward feedback, a multi-agent system that evaluates executability, static aesthetics, and interactive aesthetics. Building on this, we develop GRPO-AR, which integrates these signals into the GRPO algorithm for joint optimization of functionality and code aesthetics. Finally, we develop OpenDesign, a benchmark for assessing code aesthetics. Experimental results show that combining supervised fine-tuning on AesCode-358K with reinforcement learning using agentic reward feedback significantly improves performance on OpenDesign and also enhances results on existing benchmarks such as PandasPlotBench. Notably, our AesCoder-4B surpasses GPT-4o and GPT-4.1, and achieves performance comparable to large open-source models with 480B-685B parameters, underscoring the effectiveness of our approach.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CL"],"url":"https://arxiv.org/abs/2510.23272","pdf_url":"https://arxiv.org/pdf/2510.23272","is_open_access":true,"published_at":"2025-10-27T12:32:33Z","score":69},{"id":"ss_4a3763b99925534c0f6e21fd5e022404560a5a74","title":"Pretty Healthy Food: How and When Aesthetics Enhance Perceived Healthiness","authors":[{"name":"Linda Hagen"}],"abstract":"Marketers frequently style food to look pretty (e.g., in advertising). This article investigates how pretty aesthetics (defined by classical aesthetic principles, such as order, symmetry, and balance) influence healthiness judgments. The author proposes that prettier food is perceived as healthier, specifically because classical aesthetic features make it appear more natural. In a pilot, six main studies and four supplemental studies (total N = 4,301) across unhealthy and healthy, processed and unprocessed, and photographed and real foods alike, people judged prettier versions of the same food as healthier (e.g., more nutrients, less fat), despite equal perceived price. Even given financial stakes, people were misled by prettiness. In line with the proposed naturalness process, perceived naturalness mediated the effect; belief in a “natural = healthy” connection moderated it; expressive aesthetics, which do not evoke naturalness, did not produce the effect (despite being pretty); and reminders of artificial modification, which suppress perceived naturalness, mitigated it. Given that pretty food styling can harm consumers by misleading healthiness judgments for unhealthy foods, managers and policy makers should consider modification disclaimers as a tool to mitigate the “pretty = healthy” bias.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Psychology"],"doi":"10.1177/0022242920944384","url":"https://www.semanticscholar.org/paper/4a3763b99925534c0f6e21fd5e022404560a5a74","is_open_access":true,"citations":164,"published_at":"","score":68.92},{"id":"ss_0331ca790a23ff2148c7d9048d5c6732b13f1412","title":"Personalized Image Aesthetics Assessment with Rich Attributes","authors":[{"name":"Yuzhe Yang"},{"name":"Liwu Xu"},{"name":"Leida Li"},{"name":"Nan Qie"},{"name":"Yaqian Li"},{"name":"Peng Zhang"},{"name":"Yandong Guo"}],"abstract":"Personalized image aesthetics assessment (PIAA) is challenging due to its highly subjective nature. People's aesthetic tastes depend on diversified factors, including image characteristics and subject characters. The existing PIAA databases are limited in terms of annotation diversity, especially the subject aspect, which can no longer meet the increasing demands of PIAA research. To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31,220 images with annotations by 438 subjects. PARA features wealthy annotations, including 9 image-oriented objective attributes and 4 human-oriented subjective attributes. In addition, desensitized subject information, such as personality traits, is also provided to support study of PIAA and user portraits. A comprehensive analysis of the annotation data is provided and statistic study indicates that the aesthetic preferences can be mirrored by proposed subjective attributes. We also propose a conditional PIAA model by utilizing subject information as conditional prior. Experimental results indicate that the conditional PIAA model can outperform the control group, which is also the first attempt to demonstrate how image aesthetics and subject characters interact to produce the intricate personalized tastes on image aesthetics. We believe the database and the associated analysis would be useful for conducting next-generation PIAA study. The project page of PARA can be found at: https://cv-datasets.institutecv.com/#/data-sets.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Computer Science"],"doi":"10.1109/CVPR52688.2022.01924","url":"https://www.semanticscholar.org/paper/0331ca790a23ff2148c7d9048d5c6732b13f1412","pdf_url":"https://arxiv.org/pdf/2203.16754","is_open_access":true,"citations":96,"published_at":"","score":68.88},{"id":"ss_1027bc16f26926a911dae0409d46f93ef9d415cb","title":"Personality-Assisted Multi-Task Learning for Generic and Personalized Image Aesthetics Assessment","authors":[{"name":"Leida Li"},{"name":"Hancheng Zhu"},{"name":"Sicheng Zhao"},{"name":"Guiguang Ding"},{"name":"Weisi Lin"}],"abstract":"Traditional image aesthetics assessment (IAA) approaches mainly predict the average aesthetic score of an image. However, people tend to have different tastes on image aesthetics, which is mainly determined by their subjective preferences. As an important subjective trait, personality is believed to be a key factor in modeling individual’s subjective preference. In this paper, we present a personality-assisted multi-task deep learning framework for both generic and personalized image aesthetics assessment. The proposed framework comprises two stages. In the first stage, a multi-task learning network with shared weights is proposed to predict the aesthetics distribution of an image and Big-Five (BF) personality traits of people who like the image. The generic aesthetics score of the image can be generated based on the predicted aesthetics distribution. In order to capture the common representation of generic image aesthetics and people’s personality traits, a Siamese network is trained using aesthetics data and personality data jointly. In the second stage, based on the predicted personality traits and generic aesthetics of an image, an inter-task fusion is introduced to generate individual’s personalized aesthetic scores on the image. The performance of the proposed method is evaluated using two public image aesthetics databases. The experimental results demonstrate that the proposed method outperforms the state-of-the-arts in both generic and personalized IAA tasks.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Computer Science","Medicine"],"doi":"10.1109/TIP.2020.2968285","url":"https://www.semanticscholar.org/paper/1027bc16f26926a911dae0409d46f93ef9d415cb","is_open_access":true,"citations":152,"published_at":"","score":68.56},{"id":"ss_0b7e45f056d6949206713781ea9894ea0751da48","title":"Learning Personalized Image Aesthetics From Subjective and Objective Attributes","authors":[{"name":"Hancheng Zhu"},{"name":"Yong Zhou"},{"name":"Leida Li"},{"name":"Yaqian Li"},{"name":"Yandong Guo"}],"abstract":"Due to the widespread popularity of social media, researchers have developed a strong interest in learning the personalized image aesthetics of online users. Personalized image aesthetics assessment (PIAA) aims to study the aesthetic preferences of individual users for images, which should be affected by the properties of both users and images. Existing PIAA approaches usually use the generic aesthetics learned from images as a prior model and adapt it to PIAA models through a small number of data annotated by individual users. However, the prior model merely learns the objective attributes of images, which is agnostic to the subjective attributes of users, complicating efficient learning of the personalized image aesthetics of individual users. Therefore, we propose a personalized image aesthetics assessment method that integrates the subjective attributes of users and objective attributes of images simultaneously. To characterize these two attributes jointly, an attribute extraction module is introduced to learn users’ personality traits and image aesthetic attributes. Then, an aesthetic prior model is built from numerous individual users’ annotated data, which leverages the personality traits of users and the aesthetic attributes of rated images as prior knowledge to model both the image aesthetic distribution and users’ residual scores relative to generic aesthetics simultaneously. Finally, a PIAA model is obtained by fine-tuning the aesthetic prior model with an individual user’s annotated data. Experiments demonstrate that the proposed method is superior to existing PIAA methods in learning individual users’ personalized image aesthetics.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1109/TMM.2021.3123468","url":"https://www.semanticscholar.org/paper/0b7e45f056d6949206713781ea9894ea0751da48","is_open_access":true,"citations":43,"published_at":"","score":68.28999999999999},{"id":"ss_75975b66b5937d61ce4e62097d2be017fe585aa7","title":"AesCLIP: Multi-Attribute Contrastive Learning for Image Aesthetics Assessment","authors":[{"name":"Xiangfei Sheng"},{"name":"Leida Li"},{"name":"Pengfei Chen"},{"name":"Jinjian Wu"},{"name":"W. Dong"},{"name":"Yuzhe Yang"},{"name":"Liwu Xu"},{"name":"Yaqian Li"},{"name":"Guangming Shi"}],"abstract":"Image aesthetics assessment (IAA) aims at predicting the aesthetic quality of images. Recently, large pre-trained vision-language models, like CLIP, have shown impressive performances on various visual tasks. When it comes to IAA, a straightforward way is to finetune the CLIP image encoder using aesthetic images. However, this can only achieve limited success without considering the uniqueness of multimodal data in the aesthetics domain. People usually assess image aesthetics according to fine-grained visual attributes, e.g., color, light and composition. However, how to learn aesthetics-aware attributes from CLIP-based semantic space has not been addressed before. With this motivation, this paper presents a CLIP-based multi-attribute contrastive learning framework for IAA, dubbed AesCLIP. Specifically, AesCLIP consists of two major components, i.e., aesthetic attribute-based comment classification and attribute-aware learning. The former classifies the aesthetic comments into different attribute categories. Then the latter learns an aesthetic attribute-aware representation by contrastive learning, aiming to mitigate the domain shift from the general visual domain to the aesthetics domain. Extensive experiments have been done by using the pre-trained AesCLIP on four popular IAA databases, and the results demonstrate the advantage of AesCLIP over the state-of-the-arts. The source code will be public at https://github.com/OPPOMKLab/AesCLIP.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1145/3581783.3611969","url":"https://www.semanticscholar.org/paper/75975b66b5937d61ce4e62097d2be017fe585aa7","is_open_access":true,"citations":42,"published_at":"","score":68.25999999999999},{"id":"ss_4fea32eb784c6350ee95592340c9702d29937988","title":"Comment-Guided Semantics-Aware Image Aesthetics Assessment","authors":[{"name":"Yuzhen Niu"},{"name":"Shan-Ling Chen"},{"name":"B. Song"},{"name":"Zhixian Chen"},{"name":"Wenxi Liu"}],"abstract":"Existing image aesthetics assessment methods mainly rely on the visual features of images but ignore their rich semantics. Nowadays, with the widespread application of social media, the comments corresponding to images in the form of texts can be easily accessed and provide rich semantic information, which can be utilized to effectively complement image features. This paper proposes a comment-guided semantics-aware image aesthetics assessment method, which is built upon a multi-task learning framework for image aesthetics prediction and comment-guided semantics classification. To assist image aesthetics assessment, we first model the semantics of an image as the topic features of its corresponding comments using Latent Dirichlet Allocation. We then propose a two-stream multitask learning framework for both topic feature prediction and aesthetic score distribution prediction. Topic feature prediction task enables to infer the semantics from images, since the comments are usually unavailable during inference and comment-guided semantics can only serve as supervision during training. We further propose to deeply fuse aesthetics and semantic features using a layerwise feature fusion method. Experimental results demonstrate that the proposed method outperforms state-of-the-art image aesthetics assessment methods.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1109/TCSVT.2022.3201510","url":"https://www.semanticscholar.org/paper/4fea32eb784c6350ee95592340c9702d29937988","is_open_access":true,"citations":38,"published_at":"","score":68.14}],"total":275091,"page":1,"page_size":20,"sources":["arXiv","DOAJ","CrossRef","Semantic Scholar"],"query":"Aesthetics"}