Coarse-to-fine Semantic Segmentation from Image-level Labels

by   Longlong Jing, et al.

Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentation annotations which are needed for most methods, recently some researchers attempted to use object-level labels (e.g. bounding boxes) or image-level labels (e.g. image categories). In this paper, we propose a novel recursive coarse-to-fine semantic segmentation framework based on only image-level category labels. For each image, an initial coarse mask is first generated by a convolutional neural network-based unsupervised foreground segmentation model and then is enhanced by a graph model. The enhanced coarse mask is fed to a fully convolutional neural network to be recursively refined. Unlike existing image-level label-based semantic segmentation methods which require to label all categories for images contain multiple types of objects, our framework only needs one label for each image and can handle images contains multi-category objects. With only trained on ImageNet, our framework achieves comparable performance on PASCAL VOC dataset as other image-level label-based state-of-the-arts of semantic segmentation. Furthermore, our framework can be easily extended to foreground object segmentation task and achieves comparable performance with the state-of-the-art supervised methods on the Internet Object dataset.


page 1

page 2

page 3

page 4

page 6

page 10


STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation

Recently, significant improvement has been made on semantic object segme...

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

We propose a novel weakly-supervised semantic segmentation algorithm bas...

Concept Mask: Large-Scale Segmentation from Semantic Concepts

Existing works on semantic segmentation typically consider a small numbe...

Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation

Semantic image segmentation is a fundamental task in image understanding...

Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Semantic segmentation and instance level segmentation made substantial p...

GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

Panoptic segmentation as an integrated task of both static environmental...

Deep Learning for Semantic Part Segmentation with High-Level Guidance

In this work we address the task of segmenting an object into its parts,...

Please sign up or login with your details

Forgot password? Click here to reset