K. Bala

Publications
Influence

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Sean Bell, C. L. Zitnick, K. Bala, Ross B. Girshick
Computer Science
IEEE Conference on Computer Vision and Pattern…
14 December 2015

We present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Expand

Deep Photo Style Transfer

Fu-jun Luan, Sylvain Paris, E. Shechtman, K. Bala
Computer Science
IEEE Conference on Computer Vision and Pattern…
22 March 2017

We introduce a deep-learning approach to photographic style transfer that is at the same time broad and faithful, i.e., it handles a large variety of image content while accurately transferring the reference style. Expand

Intrinsic images in the wild

Sean Bell, K. Bala, Noah Snavely
Computer Science
TOGS
27 July 2014

We introduce Intrinsic Images in the Wild, a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes. Expand

Material recognition in the wild with the Materials in Context Database

S. Bell, P. Upchurch, Noah Snavely, K. Bala
Computer Science
IEEE Conference on Computer Vision and Pattern…
1 December 2014

We introduce a new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), and combine this dataset with deep learning to achieve material recognition and segmentation of images in the Wild. Expand

Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences

Andreas Veit, B. Kovács, S. Bell, Julian McAuley, K. Bala, Serge J. Belongie
Computer Science
IEEE International Conference on Computer Vision…
24 September 2015

We use Siamese CNNs to learn a feature transformation from images of items into a latent space that expresses compatibility across categories. Expand

Lightcuts: a scalable approach to illumination

B. Walter, S. Fernandez, A. Arbree, K. Bala, Michael Donikian, D. P. Greenberg
Computer Science
SIGGRAPH
1 July 2005

Lightcuts is a scalable framework for computing realistic illumination. It handles arbitrary geometry, non-diffuse materials, and illumination from a wide variety of sources including point lights,… Expand

Learning visual similarity for product design with convolutional neural networks

Sean Bell, K. Bala
Computer Science
ACM Trans. Graph.
27 July 2015

We use crowdsourcing to collect matching information between in-situ images and iconic product images to generate the data needed to train deep networks. Expand

Advanced global illumination

P. Dutré, P. Bekaert, K. Bala
Computer Science
6 July 2003

This book provides a fundamental understanding of global illumination algorithms. Expand

OpenSurfaces: a richly annotated catalog of surface appearance

Sean Bell, P. Upchurch, Noah Snavely, K. Bala
Computer Science
TOGS
21 July 2013

We present OpenSurfaces, a rich, labeled database consisting of thousands of examples of surfaces segmented from consumer photographs of interiors, and annotated with material parameters (reflectance, material names), texture information (surface normals, rectified textures), and contextual information (scene category, and object names). Expand

Optimistic parallelism requires abstractions

Milind Kulkarni, K. Pingali, B. Walter, Ganesh Ramanarayanan, K. Bala, L. Chew
Computer Science
CACM
1 September 2009

The problem of writing software for multicore processors is greatly simplified if we could automatically parallelize sequential programs. Although auto-parallelization has been studied for many… Expand

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