CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features
Po-han Li Sandeep P. Chinchali ufuk topcu
Multimodal encoders like CLIP excel in tasks such as zero-shot image classification and cross-modal retrieval. However, they require excessive training data.
We propose canonical similarity analysis (CSA), which uses two unimodal encoders to replicate multimodal encoders using limited data.
CSA maps unimodal features into a multimodal space, using a new similarity score to retain only the multimodal information.
CSA only involves the inference of unimodal encoders and a cubic-complexity matrix decomposition, eliminating the need for extensive GPU-based model training.
Experiments show that CSA outperforms CLIP while requiring $50,000\times$ fewer multimodal data pairs to bridge the modalities given pre-trained unimodal encoders on ImageNet classification and misinformative news caption detection.
CSA surpasses the state-of-the-art method to map unimodal features to multimodal features.
We also demonstrate the ability of  CSA with modalities beyond image and text, paving the way for future modality pairs with limited paired multimodal data but abundant unpaired unimodal data, such as lidar and text.
multimodal, representation learning, relative representations
Canonical similarity analysis (CSA) achieves CLIP-level performance in multimodal tasks using much less data. It maps unimodal features into a multimodal space without extensive GPU training and supports all modalities.