Summary of Investigating the Gestalt Principle Of Closure in Deep Convolutional Neural Networks, by Yuyan Zhang et al.
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
by Yuyan Zhang, Derya Soydaner, Fatemeh Behrad, Lisa Koßmann, Johan Wagemans
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores whether deep neural networks mimic human object recognition by applying the Gestalt principle of closure to convolutional neural networks (CNNs). A protocol is proposed to identify closure, and experiments are conducted using simple visual stimuli with increasingly removed edge sections. Well-known CNNs are evaluated on their ability to classify incomplete polygons, revealing a performance degradation as the edge removal percentage increases, indicating that current models rely heavily on complete edge information for accurate classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Do you want to know how computers recognize objects? Researchers studied if deep neural networks can recognize objects like humans do. They looked at how well-known computer vision models work when some of the object’s edges are missing. The results show that these models struggle when they don’t have all the information, just like humans do. This study helps us understand how computers perceive objects and might lead to better object recognition in the future. |
Keywords
» Artificial intelligence » Classification
