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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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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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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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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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
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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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…locust detection to prevent invasion), and art (e.g., recreational art).
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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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…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.
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…We also add state-of-the-art foundation models such as CLIP and GPT-3.5-Turbo to our benchmark.
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…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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…In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines.
…We use the game to collect 3.5K instances, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of
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…We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images.
…Our baseline model, powered by the state-of-the-art language model, shows promising results, and highlights new challenges and directions for the community to study.
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…Utilizing BenchLMM, we comprehensively evaluate state-of-the-art LMMs and reveal: 1) LMMs generally suffer performance degradation when working with other styles; 2) An LMM performs better than another
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…These experiments demonstrate that the proposed GoodDrag method compares favorably against the state-of-the-art approaches both qualitatively and quantitatively¹.
…We provide detailed analysis for the dataset design and further evaluate various state of the art baselines for solving this task.
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…Our proposed dataset is then used to further investigate the influence of image quality on several state-of-the-art approaches.
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…The data has been generated from thousands of state-of-the-art (magneto-)hydrodynamic and gravity-only N-body simulations from the CAMELS project.
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…Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new pathology images across 13 diverse patch-level datasets of 8 different sub-pathologies and cross-modal
…playing Go, generating art, ChatGPT, etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills?
…The dataset comprises 1344 images of muscle-tendon junctions recorded with 3 ultrasound imaging systems (Aixplorer V6, Esaote MyLab60, Telemed ArtUs), on 2 muscles (Lateral Gastrocnemius, Medial Gastrocnemius
…AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks.
…The classification was conducted with a state-of-art CNN model fine-tuned MobileNetv2.