pith. sign in

arxiv: 1611.07709 · v2 · pith:VJFFF43Rnew · submitted 2016-11-23 · 💻 cs.CV

Fully Convolutional Instance-aware Semantic Segmentation

classification 💻 cs.CV
keywords segmentationconvolutionalfullysemanticinstanceinstance-awaremaskaccuracy
0
0 comments X
read the original abstract

We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation and instance mask proposal. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The proposed network is highly integrated and achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. Code would be released at \url{https://github.com/daijifeng001/TA-FCN}.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Understanding Deep Learning Techniques for Image Segmentation

    cs.CV 2019-07 unverdicted novelty 1.0

    A 2019 survey that categorizes and intuitively explains major deep learning techniques for image segmentation, progressing from classical methods to modern neural architectures.