CyIN: Cyclic Informative Latent Space for Bridging Complete and Incomplete Multimodal Learning
Abstract
Multimodal machine learning, mimicking the human brain's ability to integrate various modalities has seen rapid growth. Most previous multimodal models are trained on perfectly paired multimodal input to reach optimal performance. In real-world deployments, however, the presence of modality is highly variable and unpredictable, causing the pre-trained models in suffering significant performance drops and fail to remain robust with dynamic missing modalities circumstances. In this paper, we present a novel Cyclic INformative Learning framework (CyIN) to bridge the gap between complete and incomplete multimodal learning. Specifically, we firstly build an informative latent space by adopting token- and label-level Information Bottleneck (IB) cyclically among various modalities. Capturing task-related features with variational approximation, the informative bottleneck latents are purified for more efficient cross-modal interaction and multimodal fusion. Moreover, to supplement the missing information caused by incomplete multimodal input, we propose cross-modal cyclic translation by reconstruct the missing modalities with the remained ones through forward and reverse propagation process. With the help of the extracted and reconstructed informative latents, CyIN succeeds in jointly optimizing complete and incomplete multimodal learning in one unified model. Extensive experiments on 4 multimodal datasets demonstrate the superior performance of our method in both complete and diverse incomplete scenarios.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the CyIN framework, which effectively bridges the gap between complete and incomplete multimodal learning through the use of cyclic information bottlenecks and cross-modal translation mechanisms. This innovative approach has the potential to significantly advance the field of multimodal machine learning by improving model robustness and performance in real-world scenarios.
Methodology
The proposed CyIN framework introduces a novel approach to multimodal learning by leveraging cyclic information bottlenecks to construct an informative latent space that effectively integrates complete and incomplete modalities. The methodology is well-structured, with clear definitions of token- and label-level information bottlenecks, and the cyclic translation mechanism enhances robustness against missing modalities. The use of variational approximations and cross-modal interactions is innovative, although the paper could benefit from a more detailed explanation of the cyclic translation process.
Results & evaluation
The experiments are extensive, covering four multimodal datasets with a variety of tasks, including sentiment analysis and emotion recognition. The results demonstrate superior performance compared to baseline methods, particularly in scenarios with incomplete modalities. However, the paper would benefit from additional comparisons with more recent state-of-the-art methods and a clearer presentation of the experimental results, including statistical significance.
Reproducibility
The implementation details are provided, including the use of specific datasets and models, which enhances reproducibility. The authors have made their code available on GitHub, which is a positive aspect for the community. However, further details on hyperparameter settings and training procedures could improve clarity.
Limitations
The paper acknowledges that it treats all modalities equally, which may not reflect their varying contributions in practice. Additionally, the performance of the cross-modal translators could be enhanced by integrating more advanced generative models. The authors also mention the need for broader training on diverse datasets to improve generalization.
Broader impact
The proposed framework has significant implications for real-world applications where multimodal data is often incomplete or missing. By addressing the missing modality issue, CyIN can enhance the robustness of multimodal systems in various domains, including healthcare, autonomous driving, and human-computer interaction. The main contribution of this paper is the introduction of the CyIN framework, which effectively bridges the gap between complete and incomplete multimodal learning through the use of cyclic information bottlenecks and cross-modal translation mechanisms. This innovative approach has the potential to significantly advance the field of multimodal machine learning by improving model robustness and performance in real-world scenarios.
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