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Related Experiment Video

Updated: May 25, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
07:43

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

The action similarity labeling challenge.

Orit Kliper-Gross1, Tal Hassner, Lior Wolf

  • 1Department of Mathematics and Computer Science, Weizmann Institute of Science, PO Box 26, Rehovot 76100, Israel. orit.kliper@weizmann.ac.il

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 21, 2012
PubMed
Summary

Researchers developed the Action Similarity LAbeliNg (ASLAN) database for video action recognition. This novel dataset focuses on action similarity, not classification, advancing understanding of action differences.

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Related Experiment Videos

Last Updated: May 25, 2026

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
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A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Published on: August 4, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Action recognition in videos is a growing research area.
  • Existing video datasets and benchmarks primarily focus on action classification.
  • There is a need for resources that facilitate the study of action similarity.

Purpose of the Study:

  • Introduce the Action Similarity LAbeliNg (ASLAN) database and benchmark protocols.
  • Shift the focus from action classification to action similarity.
  • Provide a resource for principled understanding of action differences and similarities.

Main Methods:

  • Collected thousands of web videos across over 400 complex action classes.
  • Developed benchmark protocols centered on action similarity (same/not-same).
  • Tested performance on unseen actions to evaluate generalization.

Main Results:

  • Established baseline results on the ASLAN benchmark.
  • Compared model performance against human performance.
  • Demonstrated the utility of the ASLAN dataset for action similarity research.

Conclusions:

  • The ASLAN database and benchmarks offer a novel approach to studying action recognition.
  • The focus on similarity provides deeper insights than traditional classification.
  • Public release of ASLAN data and tools encourages further research in action similarity techniques.