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Pattern-based Search of Epigenomic Data Using GeNemo

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Finding any Waldo with zero-shot invariant and efficient visual search.

Mengmi Zhang^1,2,3,4, Jiashi Feng³, Keng Teck Ma⁵

¹Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.

Nature Communications

|September 15, 2018

Summary

Humans can efficiently and invariantly search for natural objects in complex scenes. A new model demonstrates target localization without exhaustive sampling, generalizing to novel objects.

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Area of Science:

Cognitive Science
Computational Neuroscience
Computer Vision

Background:

Visual search in cluttered scenes is a fundamental challenge for daily perception.
Existing models often require extensive category-specific training for target matching.
Human visual search needs to be selective, invariant, efficient, and generalizable to novel objects.

Purpose of the Study:

To demonstrate efficient and invariant visual search for natural objects in complex scenes.
To develop a biologically inspired computational model for visual search.
To investigate mechanisms guiding visual search without exhaustive sampling.

Main Methods:

Human behavioral experiments on visual search.
Development of a computational model inspired by biological visual search mechanisms.
Model evaluation for efficiency, invariance, and generalization to novel objects.

Main Results:

Humans exhibit efficient and invariant visual search for natural objects.
The proposed computational model successfully locates targets without exhaustive sampling.
The model demonstrates generalization capabilities for novel object search.

Conclusions:

Human visual search in complex scenes is more efficient and invariant than previously modeled.
The biologically inspired model offers insights into integrating bottom-up and top-down signals for effective visual search.
This work advances understanding of natural object recognition and search in cluttered environments.