Instance Semantic Segmentation List

This repository contains lists of state-or-art instance semantic segmentation works. Papers and resources are listed below according to method types.

• Paper list
  • detection based
  • segmentation based

Brief introduction

Instance semantic segmentation is a area closely related to detection and semantic segmentation. In particular, it could be seen as detection plus foreground mask. But mostly it is not able to segment non-object pixels such as sky, land etc(which considered as a scene parsing task under semantic segmentation). For quick review related topics, see these survey papers:

Speed/accuracy trade-offs for modern convolutional object detectors, CVPR 2017

Survey of recent progress in semantic image segmentation with CNNs, until 201706

Dataset and benchmark here

Note: Pascal VOC could refer to different split of dataset. Original VOC12 segmentation task consists of train/val/test 1464/1449/1456 images respectively without instance information. It is designed for semantic segmentation. SDB provides instance-aware annotations for images from Pascal VOC12. And their split(8k/3k) differ from VOC, so another split from them Pasval VOC SDB val is provided, which is similar to Pascal VOC Main split(5717/5823).

1.Detection-based methods

• PAnet:Path Aggregation Network for Instance Segmentation, CVPR 2018

• MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features, CVPR 2018

• Mask R-CNN, ICCV 2017 [Detectron][Code(TF)][Code(MXNET)]

• BlitzNet: A Real-Time Deep Network for Scene Understanding, ICCV 2017 [web][code]

• FCIS: Fully Convolutional Instance-Aware Semantic Segmentation, CVPR 2017 [code]

• FastMask: Segment Multi-scale Object Candidates in One Shot, CVPR 2017 [code]

• instance sensitive fully convolutional networks, ECCV 2016

• MNC: Instance-aware Semantic Segmentation via Multi-task Network Cascades, CVPR 2016 [code]

• SharpMask: Learning to Refine Object Segments, ECCV 2016

• DeepMask: Learning to Segment Object Candidates, NIPS 2015 [code]

2.Segmentation-based methods

• SGN: Sequential Grouping Networks for Instance Segmentation, ICCV 2017

• InstanceCut: from Edges to Instances with MultiCut,CVPR 2017

• Pixelwise Instance Segmentation with a Dynamically Instantiated Network, CVPR 2017

• Deep Watershed Transform for Instance Segmentation, CVPR 2017 [code]

• Multi-scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation, CVPR 2016

3.Others

• End-to-End Instance Segmentation with Recurrent Attention, CVPR 2017

• Recurrent Instance Segmentation, ECCV 2016 [web]

• SDS:Simultaneous Detection and Segmentation, ECCV 2014 [web] [code]

• Hypercolumns for Object Segmentation and Fine-grained Localization , CVPR 2015