informatik-vortraege März 2024

informatik-vortraege@lists.rwth-aachen.de

2 Teilnehmer
2 Diskussionen

Einladung zum Informatik-Oberseminar am 08.04.2024

von Heinrichs, Benedikt

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +**************************************************************** Zeit: Montag, 8. April 2024, 13.30 Uhr Ort: Seminarraum 3, Kopernikusstraße 6 Referent: Benedikt Heinrichs M.Sc. IT Center, RWTH Aachen University Thema: Asynchronous Tracking and Description of Research Data Changes in Distributed Systems with Interoperable Metadata Abstract: With the digital revolution, the way to approach research has fundamentally changed. Suddenly, research processes created digital research data that needed to be stored. Initially, no standards for this existed, so practices diverged wildly. Consequently, data was produced that was not findable without a management system. For this reason, movements entered the picture intending to standardize these processes and define how research data should be managed. One recommendation is the FAIR Guiding Principles, which describe that research data should be findable, accessible, interoperable, and reusable. While these principles have set goals, no implementation guideline is provided since the different research areas are too diverse. Therefore, research data management (RDM) teams around the globe have created numerous implementations. Some of them are platforms like Coscine, which can manage research data and try to adhere to parts of the FAIR principles. However, such platforms face the issue that researchers want to store their research data with an enterprise-ready and openly accessible storage provider. Therefore, research data often does not move through these platforms but directly through the storage providers. This circumstance contradicts the aim of following the FAIR principles because the platforms cannot account for the research data movement and miss critical provenance information. The presented thesis aims to close that gap by providing a method to calculate the missing provenance information after changes occur. This so-called asynchronous data provenance is produced by comparing representations of research data. If the representations have changed, a new version or variant of the research data has likely been created. Representations can range from a generated hash to interoperable metadata about the research data. This interoperable metadata is created by running a pipeline that receives research data and extracts valuable information about its content. This information is annotated as interoperable metadata by following existing application profiles and ontologies. Interoperable metadata can be used to compute the similarity of research data with a method called FSS Jaccard. The created methods are integrated into a standards-based RDM system (RDMS), defined in this thesis, to show their applicability. For this standards-based RDMS, Coscine is used as a use case. Thereby, this thesis presents a method that can provide additional information about research data and close the presented gap for any standards-based RDMS. By using this method, RDM teams can come closer to supporting the implementation of the FAIR principles and improving the processes for researchers. Es laden ein: die Dozentinnen und Dozenten der Informatik

3 Monate, 1 Woche

Einladung: Informatik-Oberseminar Jonathon Luiten

von Luiten, Jonathon

+********************************************************************** * * * Einladung * * * * Informatik-Oberseminar * * * +********************************************************************** Zeit: Dienstag, 6. März 2024, 13.00 Uhr Ort: Raum 025, Mies-van-der-Rohe Str. 15 (UMIC Gebäude) Referent: Jonathon Luiten, M.Sc. Lehrstuhl Informatik 13 Thema: Dynamic 3D Representations and Robust Evaluation for Visual Tracking Abstract: Visual tracking is a core task within computer vision, one that involves understanding the motion and persistence of the dynamic world when observed in video. Building performant tracking algorithms is critical for many applications such as robotics; self-driving vehicles; virtual and augmented reality; scene-analysis for sporting, retail and construction scenarios; and content creation and editing. While other areas of computer vision such as recognition and detection have recently reached outstanding performance due to deep learning and extremely large training datasets, tracking has remained an incredibly difficult task where such approaches have not been able to achieve similar success. We argue that tracking is inherently different from these tasks and that simply scaling up compute and data is not going to be enough. In this thesis we develop what we believe to be the missing piece holding tracking back from similar success: the use of dynamic 3D representations that can be used to model the underlying scene. Furthermore, we find that the second thing holding back the field of visual tracking was the lack of adequate evaluation metrics and benchmark settings. We address these limitations by introducing novel metrics and benchmarks, which are crucial for measuring the performance of algorithms and guiding the field toward making meaningful progress. The first half of this thesis deals with approaches to lift representations for tracking to 3D, both at the level of whole objects (MOTSFusion) and at the level of infinitesimal 3D scene elements (Dynamic 3D Gaussians). Traditionally, tracking involves finding correspondences between static 2D representations in each timestep, such as pixels or bounding-boxes. Instead, we represent the world as a set of dynamic 3D representations that move around over time in order to consistently represent the same physical location in space as it moves. We reformulate tracking from a correspondence estimation problem, to an analysis-by-synthesis problem of fitting an underlying dynamic 3D model, whose motion explains changes in image content across timesteps. By using 3D representations we can better model appearance changes due to the 3D motion of the scene and the motion of the camera through the world, while also making use of intuitive physics knowledge about how objects move through the 3D world. This enables us to both obtain better tracking results, while also resulting in consistent dynamic 3D representations that are directly useful for many downstream tasks. The second half of this thesis deals with building robust metrics and benchmarks for evaluating the performance of visual tracking algorithms. For the task of Multi-Object Tracking (MOT), previous evaluation metrics have been sorely lacking, focusing only on particular aspects of tracking performance (e.g. detection or association), but not being able holistically measure improvements in tracking performance. Furthermore, tracking evaluation has been limited to settings where only a small number of fixed object classes were evaluated. We address both of these evaluation limitations by proposing the HOTA Metrics for evaluating tracking performance in a fair and holistic way, and introducing the task of Open-World Tracking for extending tracking evaluation to a open-world setting where a potentially unlimited set of object classes need to be tracked, even if they were not previously seen during training. Together, these mark a step-change in how tracking methods are evaluated and benchmarked, and allow the tracking community to make meaningful progress towards more performant and useful tracking algorithms. Overall, by developing both dynamic 3D representations for tracking and a novel set of evaluation metrics and benchmarks, this thesis provides a number of crucial missing pieces that are needed to move towards truly useful and performant tracking algorithms, and thus toward the success of the multitude of applications for which tracking is a core component. Es laden ein: die Dozentinnen und Dozenten der Informatik

4 Monate

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informatik-vortraege März 2024