%0 Journal Article
%T VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification
%A Souhail Bakkali
%A Zuheng Ming
%A Mickael Coustaty
%A Marçal Rusiñol
%A Oriol Ramos Terrades
%J Pattern Recognition
%D 2023
%V 139
%@ ISSN 0031-3203
%F Souhail Bakkali2023
%O DAG; 600.140; 600.121
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3826), last updated on Mon, 20 Nov 2023 12:04:31 +0100
%X Multimodal learning from document data has achieved great success lately as it allows to pre-train semantically meaningful features as a prior into a learnable downstream approach. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues, considering intra- and inter-modality relationships. Instead of merging features from different modalities into a common representation space, the proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities. The proposed learning objective is devised between intra- and inter-modality alignment tasks, where the similarity distribution per task is computed by contracting positive sample pairs while simultaneously contrasting negative ones in the common feature representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generalization capacity of our model on both low-scale and large-scale datasets.
%U http://refbase.cvc.uab.es/files/BMC2022.pdf
%U http://dx.doi.org/10.1016/j.patcog.2023.109419
%P 109419