Search Results for author:
Found 5 papers, 4 papers with code
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features.
Ranked #2 on
Semantic Segmentation
on NYU Depth v2
(Mean Accuracy metric)
Fully Convolutional Multi-Class Multiple Instance Learning
We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network.
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features.
Ranked #2 on
Semantic Segmentation
on SkyScapes-Lane
Do Convnets Learn Correspondence?
Convolutional neural nets (convnets) trained from massive labeled datasets have substantially improved the state-of-the-art in image classification and object detection.
Ranked #4 on
Keypoint Detection
on Pascal3D+
Caffe: Convolutional Architecture for Fast Feature Embedding
The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
