Are Multimodal Transformers Robust to Missing Modality? (2204.05454v1)

Abstract: Multimodal data collected from the real world are often imperfect due to missing modalities. Therefore multimodal models that are robust against modal-incomplete data are highly preferred. Recently, Transformer models have shown great success in processing multimodal data. However, existing work has been limited to either architecture designs or pre-training strategies; whether Transformer models are naturally robust against missing-modal data has rarely been investigated. In this paper, we present the first-of-its-kind work to comprehensively investigate the behavior of Transformers in the presence of modal-incomplete data. Unsurprising, we find Transformer models are sensitive to missing modalities while different modal fusion strategies will significantly affect the robustness. What surprised us is that the optimal fusion strategy is dataset dependent even for the same Transformer model; there does not exist a universal strategy that works in general cases. Based on these findings, we propose a principle method to improve the robustness of Transformer models by automatically searching for an optimal fusion strategy regarding input data. Experimental validations on three benchmarks support the superior performance of the proposed method.

An Expert Examination of Multimodal Transformer Robustness to Missing Modality

The manuscript titled "Are Multimodal Transformers Robust to Missing Modality?" presents a crucial investigation into the resilience of multimodal Transformer architectures when faced with incomplete modality scenarios. This research fills a knowledge gap regarding the inherent robustness of Transformer models within the context of modality-incomplete datasets—an area previously unaddressed despite the growing prominence of Transformers in multimodal learning tasks.

Key Findings and Contributions

This paper systematically studies how missing modalities impact the performance of Transformer models. Through experimental evaluation, it confirms that Transformer models exhibit sensitivity towards absent modalities, experiencing a significant drop in performance. More strikingly, it identifies that optimal fusion strategies, crucial for bolstering robustness, are highly dataset-specific. This refutes any presupposition of a universal fusion strategy applicable across various datasets and contexts.

To mitigate the sensitivity to missing modalities, the authors propose an innovative approach combining multi-task optimization and a strategic search for the optimal fusion strategy. Specifically, they implement an automatic search that adapts the fusion strategy to the dataset at hand. This dual-faceted approach not only enhances robustness but also significantly improves performance across different benchmarks.

Experimental Validation

The research conducts extensive experiments over three datasets: MM-IMDb, UPMC Food-101, and Hateful Memes. These datasets offer varied challenges in multimodal integration, from strong modality dominance in MM-IMDb and UPMC Food-101 to balanced modality reliance in Hateful Memes. Transformer robustness was evaluated by comparing performance across full and missing modalities scenarios.

The results show a marked improvement, with the proposed method outperforming baseline techniques, especially under severe modality-incomplete situations. Notably, the research demonstrates how dataset-specific fusion strategies emerge from automatic search, showcasing adaptability and customization in enhancing model performance and resilience.

Implications and Future Directions

The findings hold substantial implications for both theoretical comprehension and practical deployment of multimodal Transformers. The realization that fusion strategies must be tailored to each dataset aligns with an increasingly nuanced understanding of multimodal learning dynamics. Practically, this facilitates more robust AI systems capable of functioning under real-world conditions where data incompleteness is common due to privacy restrictions or sensor failures.

Looking forward, this research invites exploration of automatic fusion strategy refinement techniques in broader contexts, such as in generative and sequence-to-sequence tasks. As multimodal models become integral to applications in autonomous driving, healthcare diagnostics, and more, optimization for missing modalities will be essential in ensuring reliability and functionality.

Conclusion

This paper makes a technically robust contribution by delineating the specific challenges and solutions for multimodal Transformer robustness to missing modalities. It underscores the importance of adaptable fusion strategies in multimodal architectures, providing a promising pathway for improved model performance amid incomplete data circumstances—a scenario common in real-world applications. The insights presented by the authors pave the way for future advancements and refinement in AI model robustness and efficiency.