446 research outputs found
Temporal Action Segmentation: An Analysis of Modern Techniques
Temporal action segmentation (TAS) in videos aims at densely identifying
video frames in minutes-long videos with multiple action classes. As a
long-range video understanding task, researchers have developed an extended
collection of methods and examined their performance using various benchmarks.
Despite the rapid growth of TAS techniques in recent years, no systematic
survey has been conducted in these sectors. This survey analyzes and summarizes
the most significant contributions and trends. In particular, we first examine
the task definition, common benchmarks, types of supervision, and prevalent
evaluation measures. In addition, we systematically investigate two essential
techniques of this topic, i.e., frame representation and temporal modeling,
which have been studied extensively in the literature. We then conduct a
thorough review of existing TAS works categorized by their levels of
supervision and conclude our survey by identifying and emphasizing several
research gaps. In addition, we have curated a list of TAS resources, which is
available at https://github.com/nus-cvml/awesome-temporal-action-segmentation.Comment: 19 pages, 9 figures, 8 table
Lifted Bayesian filtering in multi-entity systems
This thesis focuses on Bayesian filtering for systems that consist of multiple, interacting entites (e.g. agents or objects), which can naturally be described by Multiset Rewriting Systems (MRSs). The main insight is that the state space that is underling an MRS exhibits a certain symmetry, which can be exploited to increase inference efficiency. We provide an efficient, lifted filtering algorithm, which is able to achieve a factorial reduction in space and time complexity, compared to conventional, ground filtering.Diese Arbeit betrachtet Bayes'sche Filter in Systemen, die aus mehreren, interagierenden Entitäten (z.B. Agenten oder Objekten) bestehen. Die Systemdynamik solcher Systeme kann auf natürliche Art durch Multiset Rewriting Systems (MRS) spezifiziert werden. Die wesentliche Erkenntnis ist, dass der Zustandraum Symmetrien aufweist, die ausgenutzt werden können, um die Effizienz der Inferenz zu erhöhen. Wir führen einen effizienten, gelifteten Filter-Algorithmus ein, dessen Zeit- und Platzkomplexität gegenüber dem grundierten Algorithmus um einen faktoriellen Faktor reduziert ist
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