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

TL;DR: We distinguish between different modeling paradigms for multi-modal learning from the perspective of generative models and offer a general recipe for designing models that efficiently leverage multi-modal data, leading to more accurate predictions.

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.

Prerequisites

$ pip install -r requirements.txt

📚 Datasets Overview and Instructions

Our project utilizes several datasets, each organized within specific folders. Below is an overview of the datasets and links to detailed instructions in their respective folders:

AVMNIST

• Description: Audio-Vision MNIST (AV-MNIST) combines audio and visual modalities for MNIST digit (0-9) recognition task.
• Instructions: For detailed instructions on how to use this datasets, refer to the README in the AVMNIST_and_MIMIC folder.

fastMRI

• Description: The fastMRI dataset is a large-scale dataset that consists of raw k-space knee data alongside anonymized clinical magnetic resonance (MR) images and pathology labels.
• Instructions: Detailed steps for using the fastMRI dataset can be found in the README in the fastMRI folder.

MIMIC-III

• Description: he MIMIC-III dataset encompasses ten years of intensive care unit (ICU) patient data from Beth Israel Deaconess Medical Center. The dataset is divided into two modalities: 1) time-series modality, which includes hourly medical measurements over 24 hours, and 2) static modality, capturing a patient’s medical information. We consider three tasks: a) predicting the mortality of a patient within 1 day, 2 days, 3 days, 1 week, 1 year and beyond, and b) two binary classification tasks for ICD-9 codes, one to assess if a patient falls under group 1 (codes 140-239; neoplasms) and another for group 7 (codes 460-519; diseases of respiratory system).
• Instructions: For detailed instructions on how to use this datasets, refer to the README in the AVMNIST_and_MIMIC folder.

VQA

• Description: The objective of VQA is to answer questions about images. The eval- uation encompasses the IID and nine out-of-distribution (OOD) test-sets released by VQA-VS dataset.
• Instructions: Comprehensive guidelines on these datasets are available in the README in the VQA_and_NLVR2 folder.

NLVR

• Description: NLVR2 represents a binary classification task in which the goal is to determine whether the text description correctly describes a pair of two images.
• Instructions: Comprehensive guidelines on these datasets are available in the README in the VQA_and_NLVR2 folder.

Contributing

We'd love to accept your contributions to this project. Please feel free to open an issue, or submit a pull request as necessary. If you have implementations of this repository in other ML frameworks, please reach out so we may highlight them here.

License

This codebase is released under MIT License.

📌 Citation

If you find this paper useful, please consider staring 🌟 this repo and citing 📑 our paper:

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