Learning Portrait Style Representations

• URL: http://arxiv.org/abs/2012.04153v1
• Date: Tue, 8 Dec 2020 01:36:45 GMT
• Title: Learning Portrait Style Representations
• Authors: Sadat Shaik, Bernadette Bucher, Nephele Agrafiotis, Stephen Phillips, Kostas Daniilidis, William Schmenner
• Abstract summary: We study style representations learned by neural network architectures incorporating higher level characteristics. We find variation in learned style features from incorporating triplets annotated by art historians as supervision for style similarity. We also present the first large-scale dataset of portraits prepared for computational analysis.
• Score: 34.59633886057044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Style analysis of artwork in computer vision predominantly focuses on achieving results in target image generation through optimizing understanding of low level style characteristics such as brush strokes. However, fundamentally different techniques are required to computationally understand and control qualities of art which incorporate higher level style characteristics. We study style representations learned by neural network architectures incorporating these higher level characteristics. We find variation in learned style features from incorporating triplets annotated by art historians as supervision for style similarity. Networks leveraging statistical priors or pretrained on photo collections such as ImageNet can also derive useful visual representations of artwork. We align the impact of these expert human knowledge, statistical, and photo realism priors on style representations with art historical research and use these representations to perform zero-shot classification of artists. To facilitate this work, we also present the first large-scale dataset of portraits prepared for computational analysis.

Related papers

• Style Based Clustering of Visual Artworks [2.4374097382908477]
  We introduce and deliberate over the notion of style-based clustering of visual artworks. Our main objective is to explore neural feature representations and architectures that can be used for style-based clustering.
  arXiv  Detail & Related papers  (2024-09-12T17:44:07Z)
• GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
  Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
  arXiv  Detail & Related papers  (2024-08-01T11:52:56Z)
• ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques [11.286457041998569]
  We show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model.
  arXiv  Detail & Related papers  (2023-08-29T13:15:13Z)
• A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning [84.8813842101747]
  Unified Contrastive Arbitrary Style Transfer (UCAST) is a novel style representation learning and transfer framework. We present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature. Our framework consists of three key components, i.e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
  arXiv  Detail & Related papers  (2023-03-09T04:35:00Z)
• Leveraging Computer Vision Application in Visual Arts: A Case Study on the Use of Residual Neural Network to Classify and Analyze Baroque Paintings [0.0]
  In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.
  arXiv  Detail & Related papers  (2022-10-27T10:15:36Z)
• A domain adaptive deep learning solution for scanpath prediction of paintings [66.46953851227454]
  This paper focuses on the eye-movement analysis of viewers during the visual experience of a certain number of paintings. We introduce a new approach to predicting human visual attention, which impacts several cognitive functions for humans. The proposed new architecture ingests images and returns scanpaths, a sequence of points featuring a high likelihood of catching viewers' attention.
  arXiv  Detail & Related papers  (2022-09-22T22:27:08Z)
• Arbitrary Style Transfer with Structure Enhancement by Combining the Global and Local Loss [51.309905690367835]
  We introduce a novel arbitrary style transfer method with structure enhancement by combining the global and local loss. Experimental results demonstrate that our method can generate higher-quality images with impressive visual effects.
  arXiv  Detail & Related papers  (2022-07-23T07:02:57Z)
• Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning [84.8813842101747]
  Contrastive Arbitrary Style Transfer (CAST) is a new style representation learning and style transfer method via contrastive learning. Our framework consists of three key components, i.e., a multi-layer style projector for style code encoding, a domain enhancement module for effective learning of style distribution, and a generative network for image style transfer.
  arXiv  Detail & Related papers  (2022-05-19T13:11:24Z)
• Demographic Influences on Contemporary Art with Unsupervised Style Embeddings [25.107166631583212]
  contempArt is a collection of paintings and drawings, a detailed graph network based on social connections on Instagram and additional socio-demographic information. We evaluate three methods suited for generating unsupervised style embeddings of images and correlate them with the remaining data.
  arXiv  Detail & Related papers  (2020-09-30T10:13:18Z)
• Understanding Compositional Structures in Art Historical Images using Pose and Gaze Priors [20.98603643788824]
  Image compositions are useful in analyzing the interactions in an image to study artists and their artworks. In this work, we attempt to automate this process using the existing state of the art machine learning techniques. Our approach focuses on two central themes of image composition: (a) detection of action regions and action lines of the artwork; and (b) pose-based segmentation of foreground and background.
  arXiv  Detail & Related papers  (2020-09-08T15:01:56Z)
• NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits [67.58044348082944]
  This paper proposes a new structured, three level, benchmark dataset for the evaluation of stylised portrait images. Rigorous criteria were used for its construction, and its consistency was validated by user studies. A new methodology has been developed for evaluating portrait stylisation algorithms.
  arXiv  Detail & Related papers  (2020-09-01T18:04:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.