55 citations · 115 across the 6 of their papers we have counts for
6 papers · 1 filter
Growing a Brain: Fine-Tuning by Increasing Model Capacity
Yu-Xiong Wang, Deva Ramanan, Martial Hebert
CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is t…
Shapes and Context: In-the-Wild Image Synthesis & Manipulation
Aayush Bansal, Yaser Sheikh, Deva Ramanan
We introduce a data-driven approach for interactively synthesizing in-the-wild images from semantic label maps. Our approach is dramatically different from recent work in this spac…
Tinkering Under the Hood: Interactive Zero-Shot Learning with Net Surgery
Vivek Krishnan, Deva Ramanan
We consider the task of visual net surgery, in which a CNN can be reconfigured without extra data to recognize novel concepts that may be omitted from the training set. While most…
PixelNet: Towards a General Pixel-level Architecture
Aayush Bansal, Xinlei Chen, Bryan Russell +2
We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Conv…
3D Hand Pose Detection in Egocentric RGB-D Images
Gregory Rogez, James S. Supancic, Maryam Khademi +2
We focus on the task of everyday hand pose estimation from egocentric viewpoints. For this task, we show that depth sensors are particularly informative for extracting near-field i…
Egocentric Pose Recognition in Four Lines of Code
Gregory Rogez, James S. Supancic, Deva Ramanan
We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during every…