BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Wilber, Michael J.; Chen, Fang; Jin, Hailin; Hertzmann, Aaron; Collomosse, John; Belongie, Serge

doi:10.1109/iccv.2017.136

Cited by 107 publications

(75 citation statements)

References 30 publications

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“…In [33], a robust low rank parametrized CNN model is proposed to recognise common categories in an unseen domain (photo, painting, cartoon or sketch). In [53], a new annotated database is introduced, on which it is shown that fine-tuning improves recognition performances. Several works have also successfully adapted CNNs architectures to the problem of style recognition in artworks [32,4,37].…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Object Detection in Artworks

Gonthier

Gousseau

Ladjal

et al. 2019

Lecture Notes in Computer Science

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We propose a method for the weakly supervised detection of objects in paintings. At training time, only image-level annotations are needed. This, combined with the efficiency of our multiple-instance learning method, enables one to learn new classes on-the-fly from globally annotated databases, avoiding the tedious task of manually marking objects. We show on several databases that dropping the instance-level annotations only yields mild performance losses. We also introduce a new database, IconArt, on which we perform detection experiments on classes that could not be learned on photographs, such as Jesus Child or Saint Sebastian. To the best of our knowledge, these are the first experiments dealing with the automatic (and in our case weakly supervised) detection of iconographic elements in paintings. We believe that such a method is of great benefit for helping art historians to explore large digital databases.

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Section: Related Workmentioning

confidence: 99%

Weakly Supervised Object Detection in Artworks

Gonthier

Gousseau

Ladjal

et al. 2019

Lecture Notes in Computer Science

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“…Datasets. We evaluate GDWCT with various datasets including CelebA [27], Artworks [39] (Ukiyoe, Monet, Cezanne, and Van Gogh), cat2dog [21], Pen ink and Watercolor classes of the Behance Artistic Media (BAM) [34], and Yosemite [39] (summer and winter scenes) datasets.…”

Section: Methodsmentioning

confidence: 99%

Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation

Cho¹,

Choi

Park³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

109

108

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Recently, unsupervised exemplar-based image-to-image translation, conditioned on a given exemplar without the paired data, has accomplished substantial advancements.In order to transfer the information from an exemplar to an input image, existing methods often use a normalization technique, e.g., adaptive instance normalization, that controls the channel-wise statistics of an input activation map at a particular layer, such as the mean and the variance. Meanwhile, style transfer approaches similar task to image translation by nature, demonstrated superior performance by using the higher-order statistics such as covariance among channels in representing a style. In detail, it works via whitening (given a zero-mean input feature, transforming its covariance matrix into the identity). followed by coloring (changing the covariance matrix of the whitened feature to those of the style feature). However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation. In response, this paper proposes an end-to-end approach tailored for image translation that efficiently approximates this transformation with our novel regularization methods. We further extend our approach to a group-wise form for memory and time efficiency as well as image quality. Extensive qualitative and quantitative experiments demonstrate that our proposed method is fast, both in training and inference, and highly effective in reflecting the style of an exemplar. Finally, our code is available at https://github.com/ WonwoongCho/GDWCT.

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“…[14,38] benchmark existing methods on artistic objects, but only propose improvements over older, non-convolutional techniques, which are greatly outperformed by neural networks. [37] provide a large dataset of artistic domains with per-image object annotations, but we found the labels were too coarse (due to multiple objects in the same image and no bounding box annotations) and the human annotations too sparse, to reliably perform recognition. [21] publish a dataset of objects in artistic modalities, and propose a domain generalization approach.…”

Section: Related Workmentioning

confidence: 99%

Artistic Object Recognition by Unsupervised Style Adaptation

Thomas

Kovashka

2019

Computer Vision – ACCV 2018

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Computer vision systems currently lack the ability to reliably recognize artistically rendered objects, especially when such data is limited. In this paper, we propose a method for recognizing objects in artistic modalities (such as paintings, cartoons, or sketches), without requiring any labeled data from those modalities. Our method explicitly accounts for stylistic domain shifts between and within domains. To do so, we introduce a complementary training modality constructed to be similar in artistic style to the target domain, and enforce that the network learns features that are invariant between the two training modalities. We show how such artificial labeled source domains can be generated automatically through the use of style transfer techniques, using diverse target images to represent the style in the target domain. Unlike existing methods which require a large amount of unlabeled target data, our method can work with as few as ten unlabeled images. We evaluate it on a number of cross-domain object and scene classification tasks and on a new dataset we release. Our experiments show that our approach, though conceptually simple, significantly improves the accuracy that existing domain adaptation techniques obtain for artistic object recognition.

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BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

Cited by 107 publications

References 30 publications

Weakly Supervised Object Detection in Artworks

Weakly Supervised Object Detection in Artworks

Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation

Artistic Object Recognition by Unsupervised Style Adaptation

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