Topology-Aware Representation Alignment for Semi-Supervised Vision-Language Learning
The ToMA framework uses persistent homology to align image and text representations in semi-supervised vision-language learning. It decomposes pairwise relations into H0-death (connectivity) and lightweight H1-birth (cycle structure) edges, aligning their directions across modalities. This structural alignment yields stable gains in zero-shot classification and image retrieval, proving topology is a useful signal for multimodal adaptation.