Search Results for author:
Found 6 papers, 2 papers with code
Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their poses.
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
However, it is difficult and costly to segment objects in novel categories because a large number of mask annotations is required.
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets.
PatchFCN for Intracranial Hemorrhage Detection
This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans.
From Lifestyle Vlogs to Everyday Interactions
A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data.
DeepBox: Learning Objectness with Convolutional Networks
Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct.
