Aesthetics Quality Assessment

AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity. Each of the video clips has been exhaustively annotated by human annotators, and together they represent a rich variety of scenes, recording conditions, and expressions of human activity. There are annotations for: - Kinetics (AVA-Kinetics) - a crossover between AVA and Kinetics. In order to provide localized action labels on a wider variety of visual scenes, authors provide AVA action labels on videos from Kinetics-700, nearly doubling the number of total annotations, and increasing the number of unique videos by over 500x. - Actions (AvA Actions) - the AVA dataset densely annotates 80 atomic visual actions in 430 15-minute movie clips, where actions are localized in space and time, resulting in 1.62M action labels with multiple labels per human occurring frequently. - Spoken Activity (AVA ActiveSpeaker, AVA Speech). AVA ActiveSpeaker: associates speaking activity with a visible face, on the AVA v1.0 videos, resulting in 3.65 million frames labeled across ~39K face tracks. AVA Speech densely annotates audio-based speech activity in AVA v1.0 videos, and explicitly labels 3 background noise conditions, resulting in ~46K labeled segments spanning 45 hours of data. Image Source: https://www.researchgate.net/profile/Paolo_Napoletano/publication/309327222/figure/fig1/AS:419620126248965@1477056642346/Sample-images-from-the-Aesthetic-Visual-Analysis-AVA-database-sorted-by-their-aesthetic.png

Contains aesthetic scores and meaningful attributes assigned to each image by multiple human raters. Source: Photo Aesthetics Ranking Network with Attributes and Content Adaptation

Aesthetic Visual Analysis is a dataset for aesthetic image assessment that contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style.

The image aesthetic benchmark [18] consists of 10800 Flickr photos of four categories, i.e., “animals”, “urban”, “people” and “nature”, and is constructed originally to retrieve beautiful yet unpopular images in social networks. The ground truths of the photos in the benchmark are five aesthetic grades: “Unacceptable” - images with extremely low quality, out of focus or underexposed, “Flawed” - images with some technical flaws and without any artistic value, “Ordinary” - standard quality images without technical flaws, “Professional” - professional-quality images with some artistic value, and “Exceptional” - very appealing images showing both outstanding professional quality and high artistic value. Image Source: OrdinalCLIP

To the best of our knowledge, there is no prior dataset specifically constructed for composition assessment. To support the research on this task, we build a dataset upon the existing AADB dataset, from which we collect a total of 9,958 real-world photos. We adopt a composition rating scale from 1 to 5, where a larger score indicates better composition. We make annotation guidelines for composition quality rating and train five individual raters who specialize in fine art. So for each image, we can obtain five composition scores ranging from 1 to 5. Given the subjective nature of human aesthetic activity, we perform sanity check and consistency analysis. We use 240 additional “sanity check” images during annotating to roughly verify the validness of our annotations. We also examine the consistency of composition ratings provided by five individual raters (see Supplementary). We average the composition scores as the ground-truth composition mean score for each image. More details about our CADB dataset will be elaborated in Supplementary. Besides, we observe the content bias in our CADB dataset, that is, there are some biased categories whose score distributions are concentrated in a very narrow interval. After removing 461 biased images, we split the remaining images into 8,547 training images and 950 test images, in which the test set is made less biased for better evaluation (see Supplementary). Cited from "Image Composition Assessment with Saliency-augmented Multi-pattern Pooling" Zhang, Bo and Niu, Li and Zhang, Liqing ## Citation @article{zhang2021image, title={Image Composition Assessment with Saliency-augmented Multi-pattern Pooling}, author={Zhang, Bo and Niu, Li and Zhang, Liqing}, journal={arXiv preprint arXiv:2104.03133}, year={2021} }

The Reddit Photo Critique Dataset (RPCD) contains tuples of image and photo critiques. RPCD consists of 74K images and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback. The proposed dataset differs from previous aesthetics datasets mainly in three aspects, namely * the large scale of the dataset and the extension of the comments criticizing different aspects of the image; * it contains mostly UltraHD images; * it can easily be extended to new data as it is collected through an automatic pipeline.