Gauging the Limitations of Natural Language Supervised Text-Image Metrics Learning by Iconclass Visual Concepts

Abstract

Identification of images that are close to each other in terms of their iconographical meaning requires an applicable distance measure for text-image or image-image pairs. To obtain such a measure of distance, we finetune a group of contrastive loss based text-to-image similarity models (MS-CLIP) with respect to a large number of Iconclass visual concepts by means of natural language supervised learning. We show that there are certain Iconclass concepts that actually can be learned by the models whereas other visual concepts cannot be learned. We hypothesize that the visual concepts that can be learned more easily are intrinsically different from those that are more difficult to learn and that these qualitative differences can provide a valuable orientation for future research directions in text-to-image similarity learning.