Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge Managemen

Indexable Bayesian personalized ranking for efficient top-k recommendation

Singapore National Research FoundationNational Research Foundation (NRF) Singapor

Help me help you: Interfaces for personal robots

Index Terms—HRI, mobile user interface, information theor

Help me help you: Interfaces for personal robots

Index Terms-HRI, mobile user interface, information theory I. RESEARCH PROBLEM AND A PROPOSAL The communication bottleneck between robots and people People are adept at compensating for communication limitations, changing their communicative strategies for talking to pets, babies We propose to approach this problem by accounting for limitations in robot abilities and taking advantage of already familiar human-computer interaction models, leveraging a communication model based upon Information Theory. Using this design perspective, we present three different mobile user interfaces that were fully developed and implemented on a PR2 (Personal Robot 2) [6] for task domains in navigation, perception, learning and manipulation. II. RELEVANT THEORIES We can observe parallels between human robot interaction and the interaction between humans and general complex autonomous systems. Sheridan's taxonomy of complex human-machine systems describes the following sequence of operations: (1) acquire information, (2) analyze and display information, (3) decide on an action, and (4) implement that action [7, p. 61]. This provides the groundwork for identifying the stages at which people and/or robots should lead. In the current projects, the personal robot autonomously completes steps 1, 2 and 4, and the person completes step 3. Thus, the user interface design must address how the robot analyzes and displays its sensor information and world model to the human, and how the human can effectively communicate desired actions to the robot. An analysis of our case studies in Sheridan's framework is displayed in Gold proposed using an Information Pipeline model for HRI that is based upon information theory [8], a mathematical model of communication developed for quantifying the amount of information that could be transported through a given channel. Schramm [9] developed a theory of communication that put these ideas into the context of two-way joint communications. This could be helpful when considering the large amount of overhead involved in encoding and decoding messages sent between people and robots. The focus of the projects in this paper was on designing interfaces that applied this theory to human-robot communication. With a robot encoding messages in a way that humans can understand and humans encoding messages in a way that robots can understand, communication is easy and effective. III. THE DESIGN SPACE AND THREE UIS The personal robot platform used throughout these projects is the PR2, and the robot behaviors are built using the Robot Operating System (ROS

Scalable nearest neighbour methods for high dimensional data

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbour matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbour matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this thesis, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbour algorithm and its parameters depend on the dataset characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular dataset. In order to scale to very large datasets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbour matching framework that can be used with any of the algorithms described in the thesis. All this research has been released as an open source library called FLANN (Fast Library for Approximate Nearest Neighbours), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbour matching.Science, Faculty ofComputer Science, Department ofGraduat