Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation

Abstract

A weakly-supervised semantic segmentation framework with a tied deconvolutional neural network is presented. Each deconvolution layer in the framework consists of unpooling and deconvolution operations. 'Unpooling' upsamples the input feature map based on unpooling switches defined by corresponding convolution layer's pooling operation. 'Deconvolution' convolves the input unpooled features by using convolutional weights tied with the corresponding convolution layer's convolution operation. The unpooling-deconvolution combination helps to eliminate less discriminative features in a feature extraction stage, since output features of the deconvolution layer are reconstructed from the most discriminative unpooled features instead of the raw one. This results in reduction of false positives in a pixel-level inference stage. All the feature maps restored from the entire deconvolution layers can constitute a rich discriminative feature set according to different abstraction levels. Those features are stacked to be selectively used for generating class-specific activation maps. Under the weak supervision (image-level labels), the proposed framework shows promising results on lesion segmentation in medical images (chest X-rays) and achieves state-of-the-art performance on the PASCAL VOC segmentation dataset in the same experimental condition.

References in corpus (10)

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Theano: new features and speed improvements
• Learning to Segment Object Candidates
• Learning Deconvolution Network for Semantic Segmentation
• Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation
• Fully Convolutional Multi-Class Multiple Instance Learning
• Simultaneous Detection and Segmentation
• Instance-aware Semantic Segmentation via Multi-task Network Cascades
• Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
• Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

Cited by in corpus (6)

• A Survey on Deep Learning in Medical Image Analysis
• Deep Learning in Medical Ultrasound Image Segmentation: a Review
• Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
• Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
• Learning Rich Representations For Structured Visual Prediction Tasks
• Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation