VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification

• URL: http://arxiv.org/abs/2205.12029v3
• Date: Thu, 11 May 2023 15:31:06 GMT
• Title: VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification
• Authors: Souhail Bakkali, Zuheng Ming, Mickael Coustaty, Mar\c{c}al Rusi\~nol, Oriol Ramos Terrades
• Abstract summary: 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 task. In this paper, we approach the document classification problem by learning cross-modal representations through language and vision cues. The proposed method exploits high-level interactions and learns relevant semantic information from effective attention flows within and across modalities.
• Score: 3.7798600249187295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 task. 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 joint 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 joint representation space}. Extensive experiments on public document classification datasets demonstrate the effectiveness and the generality of our model on low-scale and large-scale datasets.

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