@inproceedings{
madaan2024jointly,
title={Jointly Modeling Inter- \& Intra-Modality Dependencies for Multi-modal Learning},
author={Divyam Madaan and Taro Makino and Sumit Chopra and Kyunghyun Cho},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=XAKALzI3Gw}
}
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. The code is available at https://github.com/divyam3897/I2M2.
Jointly Modeling Inter-     Intra-Modality Dependencies for Multi-modal Learning
Divyam Madaan Taro Makino Sumit Chopra Kyunghyun Cho