Less is More: Pursuing the Visual Turing Test with the Kuleshov Effect

Olague, Gustavo; Olague, Matthieu; Jacobo-Lopez, Angel R.; Ibarra-Vazquez, Gerardo

doi:10.1109/cvprw53098.2021.00171

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…McKinstry (1997) and Geman et al (2015) considered only Boolean-type questions about a series of facts and an image scene, respectively, to provide a more quantitative measure of intelligence that is not affected by the use of trickery or guile. Olague et al (2021) metric, which captures two aspects (i.e., making sense and being specific) of human-likeness in conversation.…”

Section: Turing Testmentioning

confidence: 99%

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Video Turing Test: A first step towards human‐level AI

Lee,

Heo,

Choi

et al. 2023

AI Magazine

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The development of artificial intelligence (AI) agents capable of human‐level understanding of video content and conducting conversations with humans on this basis is a promising application that people expect. However, this is a challenging task that requires the holistic integration of multimodal information with temporal dependencies and reasoning, as well as social and physical commonsense. In addition, the development of appropriate systematic evaluation methods is essential. In this context, we introduce the Video Turing Test (VTT), a blind test used to evaluate human‐likeness in terms of video comprehension ability. Moreover, we propose Vincent as a video understanding AI. We explain the configuration of VTT, the architecture of Vincent to prepare for VTT and the proposed evaluation methods for video comprehension. We also estimate the current intelligence level of AI based on our results and discuss future research directions.

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Section: Turing Testmentioning

confidence: 99%

“…Olague et al. (2021) suggested a purely visual processing‐based Turing Test that avoids reliance on conversational ability and imitates the emotional interpretation of humans. Adams, Banavar, and Campbell (2016) presented I‐athlon as a multidimensional Turing Test.…”

Section: Introductionmentioning

confidence: 99%

Video Turing Test: A first step towards human‐level AI

Lee,

Heo,

Choi

et al. 2023

AI Magazine

View full text Add to dashboard Cite

show abstract

“…To provide a more quantitative measure of intelligence preventing passing the test by trickery or guile, (McKinstry 1997;Geman et al 2015) considered only boolean type questions about a series of facts or an image scene, respectively. To avoid reliance on conversational ability, (Olague et al 2021) suggested a purely visual processing-based Turing test which aims to imitate the emotional interpretation of humans. On the other hand, (Adiwardana et al 2020) proposed a novel evaluation metric, Sensibleness and Specificity Average to capture human-likeness of conversational ability of a machine.…”

Section: Related Work Turing Testmentioning

confidence: 99%

Toward a Human-Level Video Understanding Intelligence

Heo,

Lee,

Choi

et al. 2021

Preprint

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We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly challenging task, and evaluation methods for adequately measuring and analyzing the progress of AI agent are lacking as well. In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents. We define a general format and procedure of the Video Turing Test and present a case study to confirm the effectiveness and usefulness of the proposed test.

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Modeling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification Through Inferential Knowledge

Olague

Ibarra-Vazquez

et al. 2023

Genetic and Evolutionary Computation

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Less is More: Pursuing the Visual Turing Test with the Kuleshov Effect

Cited by 4 publications

References 26 publications

Video Turing Test: A first step towards human‐level AI

Video Turing Test: A first step towards human‐level AI

Toward a Human-Level Video Understanding Intelligence

Modeling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification Through Inferential Knowledge

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