622 citations · 1.4k across the 32 of their papers we have counts for
6 papers · 1 filter
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
Mohammadreza Mostajabi, Nicholas Kolkin, Gregory Shakhnarovich
We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories. It exploits localization cue…
Examining the Impact of Blur on Recognition by Convolutional Networks
Igor Vasiljevic, Ayan Chakrabarti, Gregory Shakhnarovich
State-of-the-art algorithms for many semantic visual tasks are based on the use of convolutional neural networks. These networks are commonly trained, and evaluated, on large annot…
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Taehwan Kim, Jonathan Keane, Weiran Wang +5
We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied par…
FractalNet: Ultra-Deep Neural Networks without Residuals
Gustav Larsson, Michael Maire, Gregory Shakhnarovich
We introduce a design strategy for neural network macro-architecture based on self-similarity. Repeated application of a simple expansion rule generates deep networks whose structu…
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
Ayan Chakrabarti, Jingyu Shao, Gregory Shakhnarovich
A single color image can contain many cues informative towards different aspects of local geometric structure. We approach the problem of monocular depth estimation by using a neur…
Learning Representations for Automatic Colorization
Gustav Larsson, Michael Maire, Gregory Shakhnarovich
We develop a fully automatic image colorization system. Our approach leverages recent advances in deep networks, exploiting both low-level and semantic representations. As many sce…