Search Results for author:
Found 9 papers, 1 papers with code
Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets.
Ranked #34 on
Semantic Segmentation
on NYU Depth v2
Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation
We use a hierarchical Lovasz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals.
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation
We use a hierarchical Lov\'asz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals.
Team PFDet's Methods for Open Images Challenge 2019
Using this method, the team PFDet achieved 3rd and 4th place in the instance segmentation and the object detection track, respectively.
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector.
PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track
We present a large-scale object detection system by team PFDet.
Minimizing Supervision for Free-space Segmentation
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
Distantly Supervised Road Segmentation
We present an approach for road segmentation that only requires image-level annotations at training time.
Pano2CAD: Room Layout From A Single Panorama Image
This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image.
