A Framework for Multi-modal Learning: Jointly Modeling Inter- & Intra-Modality Dependencies

• URL: http://arxiv.org/abs/2405.17613v1
• Date: Mon, 27 May 2024 19:22:41 GMT
• Title: A Framework for Multi-modal Learning: Jointly Modeling Inter- & Intra-Modality Dependencies
• Authors: Divyam Madaan, Taro Makino, Sumit Chopra, Kyunghyun Cho,
• Abstract summary: We argue that conventional approaches that rely solely on either inter- or intra-modality dependencies may not be optimal in general. We propose inter-language & intra-modality modeling (I2M2) framework, which captures and integrates both the inter- and intra-modality dependencies. We evaluate our approach using real-world healthcare and vision-and-the-art datasets with state-of-the-art models.
• Score: 42.16496299814368
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-modality dependencies (the relationships between different modalities and the label) or the intra-modality dependencies (the relationships within a single modality and the label). We argue that these conventional approaches that rely solely on either inter- or intra-modality dependencies may not be optimal in general. We view the multi-modal learning problem from the lens of generative models where we consider the target as a source of multiple modalities and the interaction between them. Towards that end, we propose inter- & intra-modality modeling (I2M2) framework, which captures and integrates both the inter- and intra-modality dependencies, leading to more accurate predictions. We evaluate our approach using real-world healthcare and vision-and-language datasets with state-of-the-art models, demonstrating superior performance over traditional methods focusing only on one type of modality dependency.

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