4.3k citations · 8.4k across the 5 of their papers we have counts for
5 papers
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer, Jonathan Long, Trevor Darrell
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, impro…
Fully Convolutional Multi-Class Multiple Instance Learning
Deepak Pathak, Evan Shelhamer, Jonathan Long +1
Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a nove…
Do Convnets Learn Correspondence?
Jonathan Long, Ning Zhang, Trevor Darrell
Convolutional neural nets (convnets) trained from massive labeled datasets have substantially improved the state-of-the-art in image classification and object detection. However, v…
Fully Convolutional Networks for Semantic Segmentation
Jonathan Long, Evan Shelhamer, Trevor Darrell
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, excee…
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue +5
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The…