LARE: Latent augmentation using regional embedding with vision-language model

In recent years, considerable research has been conducted on vision-language models (VLMs) that handle both image and text data; these models are being applied to diverse downstream tasks, such as “image-related chat,” “image recognition by instruction,” and “answering visual questions.” Vision-language models, such as Contrastive Language–Image Pre-training (CLIP), are also high-performance image classifiers, and are being developed into domain adaptation methods that can utilize language information to extend into unseen domains. However, because these VLMs embed images as a single point in a unified embedding space, they do not fully exploit the diverse domain performance of large-scale vision-language models. Therefore, in this study, we proposed the Latent Augmentation using Regional Embedding (LARE), which embeds the image as a region in the unified embedding space learned by the VLM. By sampling the augmented image embeddings from within this latent region, LARE enables data augmentation to various unseen domains, not just to specific unseen domains. LARE achieves robust image classification for domains in and out using augmented image embeddings to fine-tune VLMs. We demonstrate that LARE outperforms previous fine-tuning models in terms of image classification accuracy on three benchmarks. We also demonstrate that LARE is a more robust and general model that is valid under multiple conditions, such as unseen domains, small amounts of data, and imbalanced data. • LARE, a novel image classification model that applies regional embedding to VLM. • A domain adaptation method leveraging latent regional representations. • A data augmentation method generating region-based variations freely. • Demonstrates superior image classification accuracy on three benchmarks. • Robust across multiple conditions, such as unseen domains and small amounts of data.