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Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
The Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest, provides strong evidence that context and multi-scale representations improve small object detection. Expand
Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style and constrain the transformation from the input to the output to be locally affine in colorspace. Expand
Intrinsic images in the wild
This paper introduces Intrinsic Images in the Wild, a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes, and develops a dense CRF-based intrinsic image algorithm for images in the wild that outperforms a range of state-of-the-art intrinsic image algorithms. Expand
Material recognition in the wild with the Materials in Context Database
A new, large-scale, open dataset of materials in the wild, the Materials in Context Database (MINC), is introduced, and convolutional neural networks are trained for two tasks: classifying materials from patches, and simultaneous material recognition and segmentation in full images. Expand
Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences
With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions likeExpand
Advanced global illumination
If you want to design and implement a global illumination rendering system or need to use and modify an existing system for your specific purpose, this book will give you the tools and the understanding to do so. Expand
Lightcuts: a scalable approach to illumination
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
This paper learns an embedding for visual search in interior design that contains two different domains of product images: products cropped from internet scenes, and products in their iconic form and evaluates the search quantitatively and qualitatively and demonstrates high quality results. Expand
OpenSurfaces: a richly annotated catalog of surface appearance
This work uses human annotations and presents a new methodology for segmenting and annotating materials in Internet photo collections suitable for crowdsourcing (e.g., through Amazon's Mechanical Turk), and designs a multi-stage set of annotation tasks with quality checks and validation. Expand
Optimistic parallelism requires abstractions
The design and implementation of a programming abstractions that permit programmers to highlight opportunities for exploiting parallelism in sequential programs are described, and a runtime system that uses these hints to execute the program in parallel is described. Expand