People-Art is an object detection dataset which consists of people in 43 different styles. People contained in this dataset are quite different from those in common photographs. There are 42 categories of art styles and movements including Naturalism, Cubism, Socialist Realism, Impressionism, and Suprematism
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SemArt is a multi-modal dataset for semantic art understanding. SemArt is a collection of fine-art painting images in which each image is associated to a number of attributes and a textual artistic comment, such as those that appear in art catalogues or museum collections It contains 21,384 samples that provides artistic comments along with fine-art paintings and their attributes for studying semantic art understanding.
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ArtEmis is a large-scale dataset aimed at providing a detailed understanding of the interplay between visual content, its emotional effect, and explanations for the latter in language. This dataset focuses on visual art (e.g., paintings, artistic photographs) as it is a prime example of imagery created to elicit emotional responses from its viewers. ArtEmis contains 439K emotion attributions and explanations from humans, on 81K artworks from WikiArt. Paper: ArtEmis: Affective Language for Visual Art
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WikiArt contains painting from 195 different artists. The dataset has 42129 images for training and 10628 images for testing.
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ArtDL is a novel painting data set for iconography classification composed of images collected from online sources. Most of the paintings are from the Renaissance period and depict scenes or characters of Christian art.
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…The dataset IconArt dataset was introduced in the following paper : "Weakly Supervised Object Detection in Artworks" Gonthier et al. ECCV 2018 Workshop Computer Vision for Art Analysis - VISART 2018. https://wsoda.telecom-paristech.fr/ https://zenodo.org/record/4737435
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We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. ArtBench-10 has several advantages over previous artwork datasets. Firstly, it is class-balanced while most previous artwork datasets suffer from the long tail class distributions. Thirdly, ArtBench-10 is created with standardized data collection, annotation, filtering, and preprocessing procedures.
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The MAMe dataset contains images of high-resolution and variable shape of artworks from 3 different museums: The Metropolitan Museum of Art of New York The Los Angeles County Museum of Art The Cleveland Museum of Art
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Repository of a generative art dataset by computer artist Andy Lomas.
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…It consists of four domains, namely Photo (1,670 images), Art Painting (2,048 images), Cartoon (2,344 images) and Sketch (3,929 images). Each domain contains seven categories.
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Throughout the history of art, the pose—as the holistic abstraction of the human body's expression—has proven to be a constant in numerous studies. However, due to the enormous amount of data that so far had to be processed by hand, its crucial role to the formulaic recapitulation of art-historical motifs since antiquity could only be highlighted With the Poses of People in Art data set, we introduce the first openly licensed data set for estimating human poses in art and validating human pose estimators. It consists of 2,454 images from 22 art-historical depiction styles, including those that have increasingly turned away from lifelike representations of the body since the 19th century. Each image annotation, in addition to mandatory fields, provides metadata from the art-historical online encyclopedia WikiArt.
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AiTLAS: Benchmark Arena is an open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO).
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…There are 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition.
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Includes 4000 images; 200 from each of 20 categories covering different types of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor, Outdoor, Jumbled, Random, and Line drawings.
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Synscapes is a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis
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The largest and cleanest face recognition dataset Glint360K, which contains 17,091,657 images of 360,232 individuals, baseline models trained on Glint360K can easily achieve state-of-the-art performance
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…The stories were generated by two state-of-the-art visual storytelling models, each aligned to 5 human-edited versions.
…It is captured from real surveillance cameras and the person bounding boxes are obtained from state-of-the-art detection algorithm. The dataset contains 1,717 identities in total.
This dataset comprises 1344 expert annotated images of muscle-tendon junctions recorded with 3 ultrasound imaging systems (Aixplorer V6, Esaote MyLab60, Telemed ArtUs), on 2 muscles (Lateral Gastrocnemius
OCTCBVS is a benchmark dataset for testing and evaluating novel and state-of-the-art computer vision algorithms.
ImageNet-R(endition) contains art, cartoons, deviantart, graffiti, embroidery, graphics, origami, paintings, patterns, plastic objects, plush objects, sculptures, sketches, tattoos, toys, and video game
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…NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence.
…contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts
…Based on rendered scenes from the open-source Blender movie "Spring", it provides photo-realistic HD datasets with state-of-the-art visual effects and ground truth training data.
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V-D4RL provides pixel-based analogues of the popular D4RL benchmarking tasks, derived from the dm_control suite, along with natural extensions of two state-of-the-art online pixel-based continuous control
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…Three state of the art pre-trained image captioning models are used.
…For advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
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…This dataset is above 20 times larger than PASCAL3D+ and KITTI, the current state-of-the-art.
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…More specifically, there exist 3 distinct benchmark databases; Turath-Standard, Turath-Art, and Turath-UNESCO.
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…Each image is manually cropped by three expert photographers (graduate students in art whose primary medium is photography) to form three training sets. There are 1,000 photos in the dataset.
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…TUM-VIE includes challenging sequences where state-of-the art VIO fails or results in large drift. Hence, it can help to push the boundary on event-based visual-inertial algorithms.
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…The average inter-class similarity is sufficiently high to represent a challenge for the current state of the art.
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…Experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy) Project
…We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.
…locust detection to prevent invasion), and art (e.g., recreational art).
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…The point clouds provided are scanned statically with state-of-the-art equipment and contain very fine details.
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…The four domains are: Art – artistic images in the form of sketches, paintings, ornamentation, etc.; Clipart – collection of clipart images; Product – images of objects without a background and Real-World
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Falling Things (FAT) is a dataset for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics.
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…Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, etc.) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset
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…pictures of traffic signs with stickers on their surface) that can fool state-of-the-art neural network-based perception systems and clean traffic sign images without any stickers on them.
…The images were obtained by running a state-of-the-art person detector on every tenth frame of 30 movies.
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…The goal of this dataset is to boost research on exploitation of interferometric data enabling the application of state-of-the-art computer vision+NLP methods.
…We also propose a benchmark of experiments using DemogPairs over state-of-the-art deep face recognition models in order to analyze their cross-demographic behavior and potential demographic biases (see
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…This dataset poses a significant challenge to state-of-the-art vision models as merely zooming in often fails to improve their ability to classify images correctly.
DELAUNAY is a dataset of abstract paintings and non-figurative art objects labelled by the artists' names.
…We also add state-of-the-art foundation models such as CLIP and GPT-3.5-Turbo to our benchmark.
…Difficulty of exploration, using states of the art algorithms and imitation to generate data for difficult environments. Real world challenges.
…dataset is a large-scale image dataset that aims to include a diverse collection of real and synthetic images from multiple categories, including Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art including 13 GANs, 7 Diffusion, and 5 miscellaneous generators) Number of sources used for real images: 8 Categories included in the dataset: Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art
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