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Recognizing Image Style

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arxiv 1311.3715 v3 pith:WVIALO4B submitted 2013-11-15 cs.CV

Sergey Karayev , Matthew Trentacoste , Helen Han , Aseem Agarwala , Trevor Darrell , Aaron Hertzmann , Holger Winnemoeller This is my paper · ORCID
classification cs.CV
keywords styleimagelabelsannotatedapproachbestdatasetsfeatures
verification ladder T0 review T1 audit T2 compute T3 formal
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The style of an image plays a significant role in how it is viewed, but style has received little attention in computer vision research. We describe an approach to predicting style of images, and perform a thorough evaluation of different image features for these tasks. We find that features learned in a multi-layer network generally perform best -- even when trained with object class (not style) labels. Our large-scale learning methods results in the best published performance on an existing dataset of aesthetic ratings and photographic style annotations. We present two novel datasets: 80K Flickr photographs annotated with 20 curated style labels, and 85K paintings annotated with 25 style/genre labels. Our approach shows excellent classification performance on both datasets. We use the learned classifiers to extend traditional tag-based image search to consider stylistic constraints, and demonstrate cross-dataset understanding of style.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ArtSeek: Deep artwork understanding via multimodal in-context reasoning and late interaction retrieval

    cs.CV 2025-07 conditional novelty 7.0 of 10

    ArtSeek combines a retrieval-augmented vision-language model with a multitask classifier to interpret artworks from images alone, reporting state-of-the-art style classification and ArtPedia captioning scores.

  2. Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations

    cs.CV 2026-07 conditional novelty 6.0 of 10

    CANVAS learns a separate embedding subspace for each art-historical relation and, across three art datasets, beats single-space CLIP models on most retrieval and classification benchmarks.

  3. SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    SCFlow learns a reversible style-content merge and then lets the same mapping perform separation without explicit disentanglement training.

  4. Beyond Linear Bottlenecks: Spline-Based Knowledge Distillation for Culturally Diverse Art Style Classification

    cs.CV 2025-07 reject novelty 4.0 of 10

    Replacing MLP projection heads with Kolmogorov-Arnold Network heads in a dual-teacher self-supervised art-style classifier yields Top-1 accuracy gains of around 0.2 to 1.0 percentage points on WikiArt and Pandora18k, ...

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