Fine-grained knowledge about manipulable objects is well-predicted by contrastive language image pre-training

Object recognition is an important ability that relies on distinguishing between similar objects (e.g., deciding which utensil(s) to use at different stages of meal preparation). Recent work describes the finegrained organization of knowledge about manipulable objects via the study of the constituent dimensions that are most relevant to human behavior, for example, vision, manipulation, and function -based properties. A logical extension of this work concerns whether or not these dimensions are uniquely human, or can be approximated by deep learning. Here, we show that behavioral dimensions are generally well -predicted by CLIP-ViT - a multimodal network trained on a large and diverse set of image -text pairs. Moreover, this model outperforms comparison networks pre -trained on smaller, image -only datasets. These results demonstrate the impressive capacity of CLIP-ViT to approximate fine-grained object knowledge. We discuss the possible sources of this benefit relative to other models (e.g., multimodal vs. image -only pretraining, dataset size, architecture).