DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention

• URL: http://arxiv.org/abs/2209.03126v1
• Date: Wed, 7 Sep 2022 13:25:09 GMT
• Title: DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention
• Authors: Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, Hitoshi Iyatomi
• Abstract summary: Methods for extracting important information from multimodal data rely on a mid-fusion architecture. We propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets. Our concept exhibits performance that is comparable to or better than the previous set-level models.
• Score: 8.382710169577447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combines the feature representations from multiple encoders. However, as the number of modalities increases, several potential problems with the mid-fusion model structure arise, such as an increase in the dimensionality of the concatenated multimodal features and missing modalities. To address these problems, we propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets (DM$^2$S$^2$). Our set-aware concept consists of three components that capture the relationships among multiple modalities: (a) a BERT-based encoder to handle the inter- and intra-order of elements in the sequences, (b) intra-modality residual attention (IntraMRA) to capture the importance of the elements in a modality, and (c) inter-modality residual attention (InterMRA) to enhance the importance of elements with modality-level granularity further. Our concept exhibits performance that is comparable to or better than the previous set-aware models. Furthermore, we demonstrate that the visualization of the learned InterMRA and IntraMRA weights can provide an interpretation of the prediction results.

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