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

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.

• The paper introduces the I2M2 framework using a probabilistic generative model to concurrently capture both inter- and intra-modality dependencies for multi-modal learning.
• Empirical evaluation across healthcare, vision, and language tasks shows I2M2 consistently outperforms traditional methods by jointly modeling these dependencies, achieving notable accuracy gains.
• The I2M2 framework offers a flexible, data-agnostic approach that enhances robustness and generalization in multi-modal tasks without requiring prior knowledge of dependency strengths.

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

This paper presents a novel framework, termed the inter- & intra-modality modeling (I2M2) framework, for addressing the complexities of multi-modal learning. Traditional approaches in this domain have primarily focused on either inter-modality dependencies, which consider relationships between different modalities and their combined influence on the target label, or intra-modality dependencies, which focus on relationships within a single modality. This work challenges the adequacy of such isolated approaches and posits that a comprehensive framework that models both types of dependencies concurrently is essential for improving predictive performance across diverse applications.

Key Contributions

1. Unified Modeling Framework: The authors introduce a probabilistic generative model to frame multi-modal learning comprehensively. In this model, both intra-modality and inter-modality dependencies are modeled using a selection variable. This approach acknowledges that the influence of individual modalities and their interactions can vary significantly across different datasets and tasks.
2. Novel Methodology - I2M2: The paper advances the I2M2 framework which concurrently models inter- and intra-modality dependencies by leveraging a classifier for each modality and an additional classifier dedicated to capturing interactions between multiple modalities. This ensemble approach facilitates flexible and effective learning irrespective of the relative strength of inter- and intra-modality dependencies in a given dataset.
3. Categorization of Existing Approaches: The framework provides a principled basis for categorizing existing multi-modal learning methodologies. Methods focusing primarily on inter-modality interactions are often less effective when faced with sparse cross-modal information. Conversely, those emphasizing intra-modality dependencies may miss critical cross-modal interactions.

Empirical Evaluation and Strong Numerical Results

The experimental assessment involves applying I2M2 across multiple domains, including healthcare (knee MRI exams, MIMIC-III for ICD-9 code prediction), and vision-and-language tasks (VQA and NLVR2). Here, the framework consistently demonstrates superior performance over traditional methods that focus on either inter- or intra-modality dependencies:

• AV-MNIST: I2M2 improved classification accuracy by 1-2% compared to state-of-the-art multimodal fusion techniques.
• FastMRI: Remarkably, I2M2 surpassed even established methods like the root-sum-of-squares, emphasizing its potential for tasks involving low SNR conditions.
• MIMIC-III: Improved prediction accuracy in mortality and ICD-9 code prediction tasks indicates that capturing both modes of dependencies enhances robustness in clinical prediction scenarios.
• Vision-and-Language Tasks: The approach maintained or improved performance on datasets like NLVR2 and achieved notable gains on VQA-VS across in-distribution and out-of-distribution settings.

Implications and Future Directions

The research underscores the need for a balanced approach to modeling modality dependencies in multi-modal tasks. By not requiring prior knowledge about the relative strengths of dependencies in datasets, I2M2 presents a flexible learning paradigm that can be adapted to various application contexts. Practically, this leverages redundancies, enhancing robustness, particularly in scenarios with distribution shifts.

Future developments may focus on refining the approach to scale with increasing modality input sizes efficiently. Addressing the computational complexity and optimizing end-to-end training mechanisms without compromising the integrative benefits I2M2 provides remains a key area of interest. Moreover, exploring its application in real-time systems and deploying it in environments with resource constraints could offer invaluable insights into the operational scalability and practical utility of the framework.

Overall, this paper solidifies the necessity of combining various dependency modeling methods to optimize and generalize learned models, paving the way for advancements in the multi-modal learning sphere across increasingly complex tasks and datasets.