Shape-Biased Learning by Thinking Inside the Box

Abstract

Convolutional Neural Networks (CNNs) surpass human-level performance on visual object recognition and detection, but their behavior still differs from human behavior in important ways. One prominent example is that CNNs trained on ImageNet exhibit an image texture bias, while humans exhibit a strong bias toward object shape. Although CNN shape bias can be increased in various ways, e.g., using data augmentation or additional training techniques, it remains unclear what causes the strong discrepancy between human and CNN object recognition strategies. Developmental research suggests that one factor driving human shape bias is that during early childhood, toddlers tend to fill their field-of-view with close-up objects. Here, we operationalize this close-up as a zoom-in on objects during CNN training which we show increases shape bias without any additional training or data augmentation. We provide further evidence for the advantage of closeup object vision by systematically manipulating the background-object ratio during CNN training, and demonstrate a strong (inverse) correlation with shape bias. Moreover, zooming-in on objects, thereby more closely emulating child vision, not only increases shape bias but also concurrently aligns classification accuracy and shape bias between humans and CNNs. Finally, we achieve a near human-like shape bias when using a developmentally-inspired background-object ratio for training and shape bias assessment. In sum, from a simple adjustment to common image datasets - zooming-in on objects - human-like shape bias can emerge. These results suggest that taking inspiration from human learning strategies is a promising avenue for building human-aligned, efficient, and more robust vision CNNs.