3k citations · 5k across the 31 of their papers we have counts for
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Constrained Structured Regression with Convolutional Neural Networks
Deepak Pathak, Philipp Krähenbühl, Stella X. Yu +1
Convolutional Neural Networks (CNNs) have recently emerged as the dominant model in computer vision. If provided with enough training data, they predict almost any visual quantity.…
Data-dependent Initializations of Convolutional Neural Networks
Philipp Krähenbühl, Carl Doersch, Jeff Donahue +1
Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models…
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
Shiry Ginosar, Kate Rakelly, Sarah Sachs +4
Imagery offers a rich description of our world and communicates a volume and type of information that cannot be captured by text alone. Since the invention of the camera, an ever-i…
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
Tinghui Zhou, Philipp Krähenbühl, Alexei A. Efros
We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this…
Learning a Discriminative Model for the Perception of Realism in Composite Images
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman +1
What makes an image appear realistic? In this work, we are answering this question from a data-driven perspective by learning the perception of visual realism directly from large a…
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
Deepak Pathak, Philipp Krähenbühl, Trevor Darrell
We present an approach to learn a dense pixel-wise labeling from image-level tags. Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network…