318 citations · 825 across the 71 of their papers we have counts for
6 papers · 2 filters
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising
Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu
We propose a novel deep network architecture for image\\ denoising based on a Gaussian Conditional Random Field (GCRF) model. In contrast to the existing discriminative denoising m…
Robust Face Alignment Using a Mixture of Invariant Experts
Oncel Tuzel, Tim K. Marks, Salil Tambe
Face alignment, which is the task of finding the locations of a set of facial landmark points in an image of a face, is useful in widespread application areas. Face alignment is pa…
Layered Interpretation of Street View Images
Ming-Yu Liu, Shuoxin Lin, Srikumar Ramalingam +1
We propose a layered street view model to encode both depth and semantic information on street view images for autonomous driving. Recently, stixels, stix-mantics, and tiered scene…
Deep Hierarchical Parsing for Semantic Segmentation
Abhishek Sharma, Oncel Tuzel, David W. Jacobs
This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that ut…
Efficient Upsampling of Natural Images
Chinmay Hegde, Oncel Tuzel, Fatih Porikli
We propose a novel method of efficient upsampling of a single natural image. Current methods for image upsampling tend to produce high-resolution images with either blurry salient…
Unsupervised Network Pretraining via Encoding Human Design
Ming-Yu Liu, Arun Mallya, Oncel C. Tuzel +1
Over the years, computer vision researchers have spent an immense amount of effort on designing image features for the visual object recognition task. We propose to incorporate thi…