A pose-estimation network pretrained on macaque monkey images and fine-tuned on 1,000 human images outperformed a human-only benchmark on precision, recall, and F1, but not on AUC.
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
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abstract
Deep neural networks perform well in object recognition, but do they perceive objects like humans? This study investigates the Gestalt principle of closure in convolutional neural networks. We propose a protocol to identify closure and conduct experiments using simple visual stimuli with progressively removed edge sections. We evaluate well-known networks on their ability to classify incomplete polygons. Our findings reveal a performance degradation as the edge removal percentage increases, indicating that current models heavily rely on complete edge information for accurate classification. The data used in our study is available on Github.
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Monkey Transfer Learning Can Improve Human Pose Estimation
A pose-estimation network pretrained on macaque monkey images and fine-tuned on 1,000 human images outperformed a human-only benchmark on precision, recall, and F1, but not on AUC.