探究深度网络能否像人一样补全缺失轮廓，发现模型严重依赖完整边缘。
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks
- 用逐步移除边框的几何图形测试模型补全能力
- 边缘缺失率越高，分类准确率越低，最高下降超30%
- 适合关注模型感知机制与人类视觉对比的研究者
深度神经网络在物体识别中表现优异，但其感知方式是否与人类相似？本研究探究卷积神经网络中的格式塔闭合原则。我们提出一种检测闭合感知的实验协议，采用逐步移除边界的简单视觉刺激进行测试。评估了多个知名网络在不完整多边形分类任务上的表现。结果表明，随着边框移除比例增加，模型性能显著下降，说明当前模型对完整边缘信息存在高度依赖。相关数据已在Github公开。
原文摘要 · Abstract (English)
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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