GMA: Green Multi-Modal Alignment for Image-Text Retrieval

Image-text retrieval is a fundamental task in image understanding. This task aims to retrieve the most relevant information from another modality based on the given image or text. Recent approaches focus on training large neural networks to bridge the gap between visual and textual domains. However, these models are computationally expensive and not explainable regarding how the data from different modalities are aligned. End-to-end optimized models, such as large neural networks, can only output the final results, making it difficult for humans to understand the reasoning behind the model’s predictions. Hence, we propose a green learning solution, Green Multi-Modal Alignment (GMA), for computational efficiency and mathematical transparency. We reduce trainable parameters to 3% compared to fine-tuning the whole image and text encoders. Experimental results show that our model can outperform the SOTA retrieval models in text-to-image and image-to-text retrieval on the Flick30k and MS-COCO datasets. Besides, our alignment process can incorporate visual and text encoder models trained separately and generalize well to unseen image-text pairs.