Structure-aware Contrastive Learning for Diagram Understanding of Multimodal Models
This addresses a domain-specific limitation in vision-language models for diagram understanding, offering an incremental advance through tailored training strategies.
The paper tackles the problem of multimodal models like CLIP performing poorly on diagrams by introducing a structure-aware contrastive learning paradigm, resulting in substantial improvements over standard CLIP and hard negative CLIP on image-text matching and visual question answering tasks for flowcharts.
Multimodal models, such as the Contrastive Language-Image Pre-training (CLIP) model, have demonstrated remarkable success in aligning visual and linguistic representations. However, these models exhibit limitations when applied to specialised visual domains, such as diagrams, which encode structured, symbolic information distinct from that of natural imagery. In this paper, we introduce a novel training paradigm explicitly designed to enhance the comprehension of diagrammatic images within vision-language models. Our approach uses ``hard'' samples for our proposed contrastive learning that incorporates two specialised loss functions that leverage the inherent structural properties of diagrams. By integrating these objectives into model training, our method enables models to develop a more structured and semantically coherent understanding of diagrammatic content. We empirically validate our approach on a benchmark dataset of flowcharts, as a representative class of diagrammatic imagery, demonstrating substantial improvements over standard CLIP and conventional hard negative CLIP learning paradigms for both image-text matching and visual question answering tasks. Our findings underscore the significance of tailored training strategies for specialised tasks and contribute to advancing diagrammatic understanding within the broader landscape of vision-language integration.