B
Balazs Kovacs
Researcher at Cornell University
Publications - 10
Citations - 522
Balazs Kovacs is an academic researcher from Cornell University. The author has contributed to research in topics: Shape analysis (digital geometry) & Shading. The author has an hindex of 5, co-authored 10 publications receiving 415 citations.
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Proceedings ArticleDOI
Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences
TL;DR: In this paper, a Siamese Convolutional Neural Network (CNN) architecture is used to learn a feature transformation from images of items into a latent space that expresses compatibility, where training examples are pairs of items that are either compatible or incompatible.
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Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences
TL;DR: The proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together, to answer questions like 'What outfit goes well with this pair of shoes?'
Journal ArticleDOI
Intrinsic Decompositions for Image Editing
TL;DR: A new synthetic ground‐truth dataset is introduced that is used to evaluate the validity of these priors and the performance of the methods, and the performances of the different methods in the context of image‐editing applications are evaluated.
Proceedings ArticleDOI
Shading Annotations in the Wild
TL;DR: This work introduces Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery.
Proceedings ArticleDOI
Learning Material-Aware Local Descriptors for 3D Shapes
Hubert Lin,Melinos Averkiou,Evangelos Kalogerakis,Balazs Kovacs,Siddhant Ranade,Vladimir G. Kim,Siddhartha Chaudhuri,Kavita Bala +7 more
TL;DR: In this article, a projective convolutional neural network architecture is employed to learn material-aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval.