Deep Metric Learning with Soft Orthogonal Proxies

Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies that are assigned to different classes may end up being closely located in the embedding space and hence having a hard time to distinguish between positive and negative items. Alternatively, they may become highly correlated and hence provide redundant information with the model. To address these issues, we propose a novel approach that introduces Soft Orthogonality (SO) constraint on proxies. The constraint ensures the proxies to be as orthogonal as possible and hence control their positions in the embedding space. Our approach leverages Data-Efficient Image Transformer (DeiT) as an encoder to extract contextual features from images along with a DML objective. The objective is made of the Proxy Anchor loss along with the SO regularization. We evaluate our method on four public benchmarks for category-level image retrieval and demonstrate its effectiveness with comprehensive experimental results and ablation studies. Our evaluations demonstrate the superiority of our proposed approach over state-of-the-art methods by a significant margin

Constraint-Based Graphic Statics - A geometrical support for computer-aided structural equilibrium design

This thesis introduces “constraint-based graphic statics”, a geometrical support for computer-aided structural design. This support increases the freedom with which the designer interacts with the plane static equilibriums being shaped. Constraint-based graphic statics takes full advantage of geometry, both its visual expressiveness and its capacity to solve complex problems in simple terms. Accordingly, the approach builds on the two diagrams of classical graphic statics: a form diagram describing the geometry of a strut-and-tie network and a force diagram vectorially representing its inner static quilibrium. Two new devices improve the control of these diagrams: (1) nodes — considered as the only variables — are constrained within Boolean combinations of graphical regions; and (2) the user modifies these diagrams by means of successive operations whose geometric properties do not at any time jeopardise the static equilibrium of the strut-and-tie network. These two devices offer useful features, such as the ability to describe, constrain and modify any static equilibrium using purely geometric grammar, the ability to compute and handle multiple solutions to a problem at the same time, the ability to switch the hierarchy of constraint dependencies, the ability to execute dynamic conditional statements graphically, the ability to compute full interdependency and therefore the ability to remove significantly the limitations of compass-and-straightedge constructions and, finally the ability to propagate some solution domains symbolically. As a result, constraint-based graphic statics encourages the emergence of new structural design approaches that are highly interactive, precognitive and chronology-free: highly interactive because forces and geometries are simultaneously and dynamically steered by the designer; precognitive because the graphical region constraining each points marks out the set of available solutions before they are even explored by the user; and chronology-free because the deductive process undertaken by the designer can be switched whenever desired

Novel perspectives and approaches to video summarization

The increasing volume of videos requires efficient and effective techniques to index and structure videos. Video summarization is such a technique that extracts the essential information from a video, so that tasks such as comprehension by users and video content analysis can be conducted more effectively and efficiently. The research presented in this thesis investigates three novel perspectives of the video summarization problem and provides approaches to such perspectives. Our first perspective is to employ local keypoint to perform keyframe selection. Two criteria, namely Coverage and Redundancy, are introduced to guide the keyframe selection process in order to identify those representing maximum video content and sharing minimum redundancy. To efficiently deal with long videos, a top-down strategy is proposed, which splits the summarization problem to two sub-problems: scene identification and scene summarization. Our second perspective is to formulate the task of video summarization to the problem of sparse dictionary reconstruction. Our method utilizes the true sparse constraint L0 norm, instead of the relaxed constraint L2,1 norm, such that keyframes are directly selected as a sparse dictionary that can reconstruct the video frames. In addition, a Percentage Of Reconstruction (POR) criterion is proposed to intuitively guide users in selecting an appropriate length of the summary. In addition, an L2,0 constrained sparse dictionary selection model is also proposed to further verify the effectiveness of sparse dictionary reconstruction for video summarization. Lastly, we further investigate the multi-modal perspective of multimedia content summarization and enrichment. There are abundant images and videos on the Web, so it is highly desirable to effectively organize such resources for textual content enrichment. With the support of web scale images, our proposed system, namely StoryImaging, is capable of enriching arbitrary textual stories with visual content

In search of equality: Developing an equal interval Likert response scale

Attitude scales are an important component of educational and psychological research. One consideration when seeking to make valid inferences from attitudinal data is the issue of the degree to which response options can be assumed to have equal intervals. Many response options on attitudinal measures may produce ordinal-level data rather than interval. This poses a problem for the statistical tests that may be used, as many analyses assume interval-level data. It also poses an interpretational issue if the conceptual distance between response options is not the same – for example, if a researcher believes that someone who answered Agree differs the same amount from someone who answers Strongly Agree as they do from someone who answers Disagree when this may not actually be the case. As a result of the importance of equal-interval response scales, in this study I sought to design a set of equal interval Agree/Disagree Likert-type response options. To develop these response options, I first asked several hundred undergraduates to assign percentages to a series of Agree/Disagree Likert scale modifiers. I used the median percentages to create a set of equal response options as well as a set of unequal options for comparison. Next, I attached these response options to measures of Mindfulness, Conscientiousness, and Agreeableness and collected data from approximately 2,100 respondents (approximately 1850 completed the equal versions, and approximately 1850 completed the unequal versions). To assess the equal-interval nature of the data, I used the polytomous IRT graded response model to compare spacing between category boundary locations. Equidistance between the category boundary locations would provide evidence that respondents were treating the response scale as equal interval. Based on the spacing between category boundary locations, the equal response options did not produce data that was equally spaced in an absolute sense. Additionally, they did not produce data that was substantially more equally spaced than the unequal options. Based on these results, response category wording may not make a difference in the spacing of category boundary locations. However, as this was just one study with a limited population, more research in this area is needed

Mission Specification Patterns for Mobile Robots: Providing Support for Quantitative Properties

With many applications across domains as diverse as logistics, healthcare, and agriculture, service robots are in increasingly high demand. Nevertheless, the designers of these robots often struggle with specifying their tasks in a way that is both human-understandable and sufficiently precise to enable automated verification and planning of robotic missions. Recent research has addressed this problem for the functional aspects of robotic missions through the use of mission specification patterns. These patterns support the definition of robotic missions involving, for instance, the patrolling of a perimeter, the avoidance of unsafe locations within an area, or reacting to specific events. Our paper introduces a catalog of QUantitAtive RoboTic mission spEcificaTion patterns (QUARTET) that tackles the complementary and equally important challenge of specifying the reliability, performance, resource use, and other key quantitative properties of robotic missions. Identified using a methodology that included the analysis of 73 research papers published in 17 leading software engineering and robotics venues between 2014–2021, our 22 QUARTET patterns are defined in a tool-supported domain-specific language. As such, QUARTET enables: (i) the precise definition of quantitative robotic-mission requirements; and (ii) the translation of these requirements into probabilistic reward computation tree logic (PRCTL), and thus their formal verification and the automated planning of robotic missions. We demonstrate the applicability of QUARTET by showing that it supports the specification of over 95% of the quantitative robotic mission requirements from a systematically selected set of recent research papers, of which 75% can be automatically translated into PRCTL for the purposes of verification through model checking and mission planning

Enhanced online programming for industrial robots

The use of robots and automation levels in the industrial sector is expected to grow, and is driven by the on-going need for lower costs and enhanced productivity. The manufacturing industry continues to seek ways of realizing enhanced production, and the programming of articulated production robots has been identified as a major area for improvement. However, realizing this automation level increase requires capable programming and control technologies. Many industries employ offline-programming which operates within a manually controlled and specific work environment. This is especially true within the high-volume automotive industry, particularly in high-speed assembly and component handling. For small-batch manufacturing and small to medium-sized enterprises, online programming continues to play an important role, but the complexity of programming remains a major obstacle for automation using industrial robots. Scenarios that rely on manual data input based on real world obstructions require that entire production systems cease for significant time periods while data is being manipulated, leading to financial losses. The application of simulation tools generate discrete portions of the total robot trajectories, while requiring manual inputs to link paths associated with different activities. Human input is also required to correct inaccuracies and errors resulting from unknowns and falsehoods in the environment. This study developed a new supported online robot programming approach, which is implemented as a robot control program. By applying online and offline programming in addition to appropriate manual robot control techniques, disadvantages such as manual pre-processing times and production downtimes have been either reduced or completely eliminated. The industrial requirements were evaluated considering modern manufacturing aspects. A cell-based Voronoi generation algorithm within a probabilistic world model has been introduced, together with a trajectory planner and an appropriate human machine interface. The robot programs so achieved are comparable to manually programmed robot programs and the results for a Mitsubishi RV-2AJ five-axis industrial robot are presented. Automated workspace analysis techniques and trajectory smoothing are used to accomplish this. The new robot control program considers the working production environment as a single and complete workspace. Non-productive time is required, but unlike previously reported approaches, this is achieved automatically and in a timely manner. As such, the actual cell-learning time is minimal