GRAM: Global Reasoning for Multi-Page VQA

Tsachi Blau, Sharon Fogel, Roi Ronen, Alona Golts, Roy Ganz, Elad Ben Avraham, Aviad Aberdam, Shahar Tsiper, Ron Litman; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 15598-15607

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA) leading methods focus on the single-page setting while documents can span hundreds of pages. We present GRAM a method that seamlessly extends pre-trained single-page models to the multi-page setting without requiring computationally-heavy pretraining. To do so we leverage a single-page encoder for local page-level understanding and enhance it with document-level designated layers and learnable tokens facilitating the flow of information across pages for global reasoning. To enforce our model to utilize the newly introduced document tokens we propose a tailored bias adaptation method. For additional computational savings during decoding we introduce an optional compression stage using our compression-transformer (CFormer)reducing the encoded sequence length thereby allowing a tradeoff between quality and latency. Extensive experiments showcase GRAM's state-of-the-art performance on the benchmarks for multi-page DocVQA demonstrating the effectiveness of our approach.