Exploring Context with Deep Structured models for Semantic Segmentation

State-of-the-art semantic image segmentation methods are mostly based on training deep convolutional neural networks (CNNs). In this work, we proffer to improve semantic segmentation with the use of contextual information. In particular, we explore `patch-patch' context and `patch-background' context in deep CNNs. We formulate deep structured models by combining CNNs and Conditional Random Fields (CRFs) for learning the patch-patch context between image regions. Specifically, we formulate CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied in order to avoid repeated expensive CRF inference during the course of back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image inputs and sliding pyramid pooling is very effective for improving performance. We perform comprehensive evaluation of the proposed method. We achieve new state-of-the-art performance on a number of challenging semantic segmentation datasets including $NYUDv2$, $PASCAL$-$VOC2012$, $Cityscapes$, $PASCAL$-$Context$, $SUN$-$RGBD$, $SIFT$-$flow$, and $KITTI$ datasets. Particularly, we report an intersection-over-union score of $77.8$ on the $PASCAL$-$VOC2012$ dataset.Comment: 16 pages. Accepted to IEEE T. Pattern Analysis & Machine Intelligence, 2017. Extended version of arXiv:1504.0101

Deep learning based RGB-D vision tasks

Depth is an important source of information in computer vision. However, depth is usually discarded in most vision tasks. In this thesis, we study the tasks of estimating depth from single monocular images, and incorporating depth for object detection and semantic segmentation. Recently, a significant number of breakthroughs have been introduced to the vision community by deep convolutional neural networks (CNNs). All of our algorithms in this thesis are built upon deep CNNs. The first part of this thesis addresses the task of incorporating depth for object detection and semantic segmentation. The aim is to improve the performance of vision tasks that are only based on RGB data. Two approaches for object detection and two approaches for semantic segmentation are presented. These approaches are based on existing depth estimation, object detection and semantic segmentation algorithms. The second part of this thesis addresses the task of depth estimation. Depth estimation is often formulated as a regression task due to the continuous property of depths. Deep CNNs for depth estimation are trained by iteratively minimizing regression errors between predicted and ground-truth depths. A drawback of regression is that it predicts depths without confidence. In this thesis, we propose to formulate depth estimation as a classification task which naturally predicts depths with confidence. The confidence can be used during training and post-processing. We also propose to exploit ordinal depth relationships from stereo videos to improve the performance of metric depth estimation. By doing so we propose a Relative Depth in Stereo (RDIS) dataset that is densely annotated with relative depths.Thesis (Ph.D.) -- University of Adelaide,School of Computer Science , 201

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure