New York University researchers propose intermodal and intramodal (I2M2) models for multimodal learning, capturing both intermodal and intramodal dependencies

In supervised multimodal learning, data from multiple modalities is mapped to a target label using information about the boundaries between the modalities. Different fields have been interested in this topic: autonomous vehicles, healthcare, robots and many more. Although multimodal learning is a fundamental paradigm in machine learning, its effectiveness differs depending on the task at hand. In some situations, a multimodal learner performs better than a unimodal learner. Still, in other cases, it may not be better than a single unimodal learner or a combination of just two. These contradictory findings highlight the need for a guiding framework to clarify the reasons behind performance gaps between multimodal models and establish a standard procedure for developing models that best utilize multimodal data.

Researchers from New York University, Genentech, and CIFAR are embarking on a groundbreaking journey to resolve these inconsistencies. They are introducing a novel, more principled approach to multimodal learning, one that has never been explored before, and are identifying the underlying variables that cause them. Using a unique probabilistic perspective, they propose a mechanism that generates data and examines the problem of supervised multimodal learning.

Since this selection variable produces interdependence between the modalities and the label, it is always set to one. The effectiveness of this selection mechanism differs between data sets. Dependencies between modalities and labels, known as intermodal dependencies, are amplified in cases of strong selection effects. In contrast, when the impact of selection is modest, intramodality dependencies (dependencies between individual modalities and the label) become increasingly important.

The proposed paradigm assumes that labels are the primary source of modality-specific data. Furthermore, it specifies the connection between the label, the selection process and the different modalities. From one use case to another, the amount to which the result depends on data from different modalities and the relationships between them vary. A multimodal system has to simulate inter- and intramodal dependencies because it is important to know how strong these dependencies are with respect to the final objective. The team achieved this by developing and merging classifiers for each modality to capture dependencies within each modality and a classifier to capture dependencies between the output label and interactions between different modes.

The I2M2 method is derived from the multimodal generative model, a widely used approach in multimodal learning. However, previous research on multimodal learning can be divided into two groups using the suggested framework. Cross-modal modeling methods, which are grouped into the first group, rely heavily on the detection of cross-modal relationships to predict the target. Despite their theoretical ability to capture connections between and within modalities, they often fail in practice due to unfulfilled assumptions about the multimodal model of learning generation. The methods used in intramodality modeling, which belong to the second group, rely only on labels for interactions between modalities, which limits their effectiveness.

Contrary to the goal of multimodal learning, these methods fail to capture the interdependence of prediction modalities. When predicting the label, cross-modal methods work well when modalities exchange substantial information, but intra-modality methods work well when information between modalities is sparse or non-existent.

Since it is not necessary to know in advance how strong these dependencies are, the suggested I2M2 architecture overcomes this drawback. Because it explicitly describes the interdependence between and within modalities, it can be adapted to different contexts and still be effective. The results demonstrate that I2M2 is not only superior, but a game-changer, for both intramodality and intermodality approaches by validating researchers' claims on various data sets. Automatic diagnosis using knee MRI scans and mortality and ICD-9 code prediction in the MIMIC-III dataset are two examples of the many healthcare jobs to which this technology is applied. Findings on vision and language tasks like NLVR2 and VQA further demonstrate the transformative potential of I2M2.

The dependencies differ in intensity between data sets, as our comprehensive evaluation indicates; the fastMRI dataset benefits more from intramodality dependencies, while the NLVR2 dataset finds more relevance in intermodality dependencies. The AV-MNIST, MIMIC-III, and VQA datasets are affected by both dependencies. I2M2 succeeds in all aspects, ensuring solid performance regardless of the relative importance of its dependencies. This extensive research and strong findings instill confidence in the effectiveness of I2M2.

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