scholarly journals Physical event representations: Observers spontaneously impose discrete temporal structure in intuitive physical scene understanding

2021 ◽  
Vol 21 (9) ◽  
pp. 2672
Author(s):  
Shannon Yasuda ◽  
Tristan S. Yates ◽  
Ilker Yildirim
2016 ◽  
Vol 17 (1) ◽  
pp. 48-76 ◽  
Author(s):  
Anne-Laure Mealier ◽  
Grégoire Pointeau ◽  
Peter Gärdenfors ◽  
Peter Ford Dominey

Abstract In robotics research with language-based interaction, simplifications are made, such that a given event can be described in a unique manner, where there is a direct mapping between event representations and sentences that can describe these events. However, common experience tells us that the same physical event can be described in multiple ways, depending on the perspective of the speaker. The current research develops methods for representing events from multiple perspectives, and for choosing the perspective that will be used for generating a linguistic construal, based on attentional processes in the system. The multiple perspectives are based on the principle that events can be considered in terms of the force driving the event, and the result obtained from the event, based on the theory of Gärdenfors. In addition, within these perspectives a further refinement can be made with respect to the agent, object, and recipient perspectives. We develop a system for generating appropriate construals of meaning, and demonstrate how this can be used in a realistic dialogic interaction between a behaving robot and a human interlocutor.


2012 ◽  
Author(s):  
Laurent Itti ◽  
Nader Noori ◽  
Lior Elazary

2019 ◽  
Vol 23 (1) ◽  
pp. 147-159
Author(s):  
Shagan Sah ◽  
Thang Nguyen ◽  
Ray Ptucha

Erkenntnis ◽  
2021 ◽  
Author(s):  
Camden Alexander McKenna

AbstractI argue for constraining the nomological possibility space of temporal experiences and endorsing the Succession Requirement for agents. The Succession Requirement holds that the basic structure of temporal experience must be successive for agentive subjects, at least in worlds that are law-like in the same way as ours. I aim to establish the Succession Requirement by showing non-successively experiencing agents are not possible for three main reasons, namely that they (1) fail to stand in the right sort of causal relationship to the outcomes of their actions, (2) exhibit the wrong sort of epistemic status for agency, and (3) lack the requisite agentive mental attitude of intentionality. I conclude that agency is incompatible with non-successive experience and therefore we should view the successive temporal structure of experience as a necessary condition for agency. I also suggest that the Succession Requirement may actually extend beyond my main focus on agency, offering preliminary considerations in favor of seeing successive experience as a precondition for selfhood as well. The consequences of the Succession Requirement are wide-ranging, and I discuss various implications for our understanding of agency, the self, time consciousness, and theology, among other things.


2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.


2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
Abhishek V. Potnis ◽  
Surya S. Durbha ◽  
Rajat C. Shinde

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.


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