Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval

• URL: http://arxiv.org/abs/2503.01980v1
• Date: Mon, 03 Mar 2025 19:01:17 GMT
• Title: Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval
• Authors: Davide Caffagni, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara,
• Abstract summary: Cross-modal retrieval is gaining increasing efficacy and interest from the research community.<n>In this paper, we design an approach that allows for multimodal queries composed of both an image and a text.<n>Our model, ReT, employs multi-level representations extracted from different layers of both visual and textual backbones.
• Score: 44.008094698200026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-modal retrieval is gaining increasing efficacy and interest from the research community, thanks to large-scale training, novel architectural and learning designs, and its application in LLMs and multimodal LLMs. In this paper, we move a step forward and design an approach that allows for multimodal queries, composed of both an image and a text, and can search within collections of multimodal documents, where images and text are interleaved. Our model, ReT, employs multi-level representations extracted from different layers of both visual and textual backbones, both at the query and document side. To allow for multi-level and cross-modal understanding and feature extraction, ReT employs a novel Transformer-based recurrent cell that integrates both textual and visual features at different layers, and leverages sigmoidal gates inspired by the classical design of LSTMs. Extensive experiments on M2KR and M-BEIR benchmarks show that ReT achieves state-of-the-art performance across diverse settings. Our source code and trained models are publicly available at https://github.com/aimagelab/ReT.

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