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Related Experiment Video

Updated: Dec 27, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

A dataset for evaluating one-shot categorization of novel object classes.

Yaniv Morgenstern¹, Filipp Schmidt¹, Roland W Fleming¹

¹Department of Experimental Psychology, Justus-Liebig University Giessen, Giessen, 35394, Germany.

|March 7, 2020

Summary

This study introduces a novel object classification dataset from human responses. It aims to bridge the gap in few-shot learning between humans and artificial intelligence (AI) systems.

Keywords:

Abstraction Categorization Generalization Machine vision Objects One-shot learning Shape Visual perception

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Last Updated: Dec 27, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

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Published on: February 8, 2019

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

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Published on: November 20, 2018

Area of Science:

Cognitive Science
Computer Vision
Machine Learning

Background:

Deep convolutional neural networks (CNNs) excel at object categorization, mirroring biological vision systems.
Despite extensive training, CNNs struggle with generalization and are vulnerable to adversarial attacks, unlike humans who generalize from few samples.

Purpose of the Study:

To present a dataset of human responses to novel object classifications.
To facilitate understanding of the few-shot learning gap between human and machine generalization.
To establish a benchmark for evaluating generalization in machine learning networks.

Main Methods:

Collected thousands of crowd-sourced human responses to novel objects.
Objects were presented with either 1 or 16 context samples.
The dataset includes human decisions and corresponding stimuli.

Main Results:

The dataset captures human performance in novel object classification under varying sample conditions.
Provides data for analyzing human generalization capabilities with limited examples.

Conclusions:

The dataset serves as a valuable resource for understanding human few-shot category learning.
It offers a benchmark for assessing and improving generalization in machine learning models, particularly in computer vision.