Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!

• URL: http://arxiv.org/abs/2010.06572v1
• Date: Tue, 13 Oct 2020 17:45:28 GMT
• Title: Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!
• Authors: Jack Hessel and Lillian Lee
• Abstract summary: Cross-modal modeling seems crucial in multimodal tasks, such as visual question answering. We propose a new diagnostic tool, empirical multimodally-additive function projection (EMAP), for isolating whether or not cross-modal interactions improve performance for a given model on a given task. For seven image+text classification tasks (on each of which we set new state-of-the-art benchmarks), we find that, in many cases, removing cross-modal interactions results in little to no performance degradation.
• Score: 26.215781778606168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling expressive cross-modal interactions seems crucial in multimodal tasks, such as visual question answering. However, sometimes high-performing black-box algorithms turn out to be mostly exploiting unimodal signals in the data. We propose a new diagnostic tool, empirical multimodally-additive function projection (EMAP), for isolating whether or not cross-modal interactions improve performance for a given model on a given task. This function projection modifies model predictions so that cross-modal interactions are eliminated, isolating the additive, unimodal structure. For seven image+text classification tasks (on each of which we set new state-of-the-art benchmarks), we find that, in many cases, removing cross-modal interactions results in little to no performance degradation. Surprisingly, this holds even when expressive models, with capacity to consider interactions, otherwise outperform less expressive models; thus, performance improvements, even when present, often cannot be attributed to consideration of cross-modal feature interactions. We hence recommend that researchers in multimodal machine learning report the performance not only of unimodal baselines, but also the EMAP of their best-performing model.

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