Deep Learning for 2D and 3D Scene Understanding

This thesis comprises a body of work that investigates the use of deep learning for 2D and 3D scene understanding. Although there has been significant progress made in computer vision using deep learning, a lot of that progress has been relative to performance benchmarks, and for static images; it is common to find that good performance on one benchmark does not necessarily mean good generalization to the kind of viewing conditions that might be encountered by an autonomous robot or agent. In this thesis, we address a variety of problems motivated by the desire to see deep learning algorithms generalize better to robotic vision scenarios. Specifically, we span topics of multi-object detection, unsupervised domain adaptation for semantic segmentation, video object segmentation, and semantic scene completion. First, most modern object detectors use a final post-processing step known as Non-maximum suppression (GreedyNMS). This suffers an inevitable trade-off between precision and recall in crowded scenes. To overcome this limitation, we propose a Pairwise-NMS to cure GreedyNMS. Specifically, a pairwise-relationship network that is based on deep learning is learned to predict if two overlapping proposal boxes contain two objects or zero/one object, which can handle multiple overlapping objects effectively. A common issue in training deep neural networks is the need for large training sets. One approach to this is to use simulated image and video data, but this suffers from a domain gap wherein the performance on real-world data is poor relative to performance on the simulation data. We target a few approaches to addressing so-called domain adaptation for semantic segmentation: (1) Single and multi-exemplars are employed for each class in order to cluster the per-pixel features in the embedding space; (2) Class-balanced self-training strategy is utilized for generating pseudo labels in the target domain; (3) Moreover, a convolutional adaptor is adopted to enforce the features in the source domain and target domain are closed with each other. Next, we tackle the video object segmentation by formulating it as a meta-learning problem, where the base learner aims to learn semantic scene understanding for general objects, and the meta learner quickly adapts the appearance of the target object with a few examples. Our proposed meta-learning method uses a closed-form optimizer, the so-called \ridge regression", which is conducive to fast and better training convergence. One-shot video object segmentation (OSVOS) has the limitation to \overemphasize" the generic semantic object information while \diluting" the instance cues of the object(s), which largely block the whole training process. Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network, the network is then better prepared for the online fine-tuning. Next, we introduce a light-weight Dimensional Decomposition Residual network (DDR) for 3D dense prediction tasks. The novel factorized convolution layer is effective for reducing the network parameters, and the proposed multi-scale fusion mechanism for depth and color image can improve the completion and segmentation accuracy simultaneously. Moreover, we propose PALNet, a novel hybrid network for Semantic Scene Completion(SSC) based on single depth. PALNet utilizes a two-stream network to extract both 2D and 3D features from multi-stages using fine-grained depth information to eficiently capture the context, as well as the geometric cues of the scene. Position Aware Loss (PA-Loss) considers Local Geometric Anisotropy to determine the importance of different positions within the scene. It is beneficial for recovering key details like the boundaries of objects and the corners of the scene. Finally, we propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Vision for Scene Understanding

This manuscript covers my recent research on vision algorithms for scene understanding, articulated in 3 research axes: 3D Vision, Weakly supervised vision, and Vision and physics. At the core of the most recent works is weakly-supervised learning and physics-embodied vision, which address short comings of supervised learning that requires large amount of data. The use of more physically grounded algorithms appears evidently beneficial as both robots and humans naturally evolve in a 3D physical world. On the other hand, accounting for physics knowledge reflects important cue about lighting and weather conditions of the scene central in my work. Physics-informed machine learning is not only beneficial for increased interpretability but also to compensate labels and data scarcity

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Emerging Technologies

This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

Tracking hands in action for gesture-based computer input

This thesis introduces new methods for markerless tracking of the full articulated motion of hands and for informing the design of gesture-based computer input. Emerging devices such as smartwatches or virtual/augmented reality glasses are in need of new input devices for interaction on the move. The highly dexterous human hands could provide an always-on input capability without the actual need to carry a physical device. First, we present novel methods to address the hard computer vision-based hand tracking problem under varying number of cameras, viewpoints, and run-time requirements. Second, we contribute to the design of gesture-based interaction techniques by presenting heuristic and computational approaches. The contributions of this thesis allow users to effectively interact with computers through markerless tracking of hands and objects in desktop, mobile, and egocentric scenarios.Diese Arbeit stellt neue Methoden für die markerlose Verfolgung der vollen Artikulation der Hände und für die Informierung der Gestaltung der Gestik-Computer-Input. Emerging-Geräte wie Smartwatches oder virtuelle / Augmented-Reality-Brillen benötigen neue Eingabegeräte für Interaktion in Bewegung. Die sehr geschickten menschlichen Hände konnten eine immer-on-Input-Fähigkeit, ohne die tatsächliche Notwendigkeit, ein physisches Gerät zu tragen. Zunächst stellen wir neue Verfahren vor, um das visionbasierte Hand-Tracking-Problem des Hardcomputers unter variierender Anzahl von Kameras, Sichtweisen und Laufzeitanforderungen zu lösen. Zweitens tragen wir zur Gestaltung von gesture-basierten Interaktionstechniken bei, indem wir heuristische und rechnerische Ansätze vorstellen. Die Beiträge dieser Arbeit ermöglichen es Benutzern, effektiv interagieren mit Computern durch markerlose Verfolgung von Händen und Objekten in Desktop-, mobilen und egozentrischen Szenarien