Enhancing Robustness of Multimodal Learning Models to Handle Missing Modalities via Parameter-Efficient Adaptation

核心概念

Multimodal learning models can be made robust to missing modalities through a simple and parameter-efficient adaptation procedure that modulates the intermediate features of available modalities to compensate for the missing ones.

摘要

The key highlights and insights from the content are:

Multimodal learning (MML) models often suffer significant performance degradation when one or more input modalities are missing at test time. Existing approaches to address this issue either require specialized training strategies or additional models/sub-networks, which are not feasible for practical deployment.

The authors propose a parameter-efficient adaptation procedure to enhance the robustness of pretrained MML models to handle missing modalities. The adaptation is achieved by inserting lightweight, learnable layers that modulate the intermediate features of the available modalities to compensate for the missing ones.

The adapted models demonstrate notable performance improvements over the original MML models when tested with missing modalities, and in many cases, outperform or match the performance of models trained specifically for each modality combination.

The adaptation approach is versatile and can be applied to a wide range of MML tasks and datasets, including semantic segmentation, material segmentation, action recognition, sentiment analysis, and classification.

The adaptation requires a very small number of additional learnable parameters (less than 1% of the total model parameters), making it computationally efficient and practical for real-world deployment.

Experiments show that the adapted models are able to learn better feature representations to handle missing modalities, as evidenced by higher cosine similarity between the features of the adapted model and the complete modality features compared to the pretrained model.

Overall, the proposed parameter-efficient adaptation approach offers a promising solution to enhance the robustness of MML models to missing modalities without the need for specialized training or additional models.

統計資料

The authors provide the following key statistics and figures to support their findings:

On the MFNet dataset for multimodal semantic segmentation, the adapted model shows a 15.41% and 1.51% improvement in performance compared to the pretrained model when Thermal and RGB are missing, respectively.
On the NYUDv2 dataset, the adapted model shows a 31.46% and 1.63% improvement in performance compared to the pretrained model when RGB and Depth are missing, respectively.
On the MCubeS dataset for multimodal material segmentation, the adapted model shows a 8.11% to 1.82% improvement in performance compared to the pretrained model for different missing modality scenarios.
On the NTU RGB+D dataset for multimodal action recognition, the adapted model shows a 7.03% and 1.06% improvement over the state-of-the-art ActionMAE and UMDR models when RGB and Depth are missing, respectively.
On the UPMC Food-101 dataset for multimodal classification, the adapted model outperforms the prompt-based approach by 1.29% on average.

引述

None.

從以下內容提煉的關鍵洞見

Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation

by Md Kaykobad ... 於 arxiv.org 09-18-2024

https://arxiv.org/pdf/2310.03986.pdf

深入探究

How can the proposed adaptation approach be extended to handle more complex missing modality scenarios, such as when multiple modalities are missing or when the missing modalities change dynamically during inference?

The proposed adaptation approach can be extended to handle more complex missing modality scenarios by implementing a dynamic feature modulation mechanism that can adapt in real-time to the presence or absence of multiple modalities. This could involve the following strategies:

Dynamic Modulation Layers: Instead of using a fixed set of modulation parameters for each modality combination, the model could incorporate a mechanism to learn and update modulation parameters dynamically based on the available modalities at inference time. This could be achieved through a gating mechanism that activates specific modulation layers depending on the detected input modalities.

Hierarchical Feature Fusion: For scenarios where multiple modalities are missing, a hierarchical feature fusion approach could be employed. This would involve creating a multi-level fusion architecture that first combines features from available modalities at a lower level and then progressively integrates these features at higher levels, allowing the model to maintain robustness even when several modalities are absent.

Contextual Awareness: The adaptation approach could be enhanced with contextual awareness, where the model learns to recognize patterns in the data that indicate which modalities are likely to be missing. By leveraging historical data or contextual cues, the model could preemptively adjust its feature modulation strategy to better handle the expected missing modalities.

Multi-Task Learning: Incorporating multi-task learning could also be beneficial. By training the model on related tasks that involve different combinations of modalities, the model can learn to generalize better to unseen combinations of missing modalities, thus improving its robustness in dynamic scenarios.

By implementing these strategies, the adaptation approach can become more flexible and capable of addressing the challenges posed by complex missing modality scenarios.

What are the potential limitations or drawbacks of the feature modulation-based adaptation approach, and are there alternative parameter-efficient adaptation techniques that could be explored?

While the feature modulation-based adaptation approach offers significant advantages in terms of parameter efficiency and flexibility, it does have potential limitations:

Limited Expressiveness: The reliance on simple linear transformations (scaling and shifting) may limit the expressiveness of the model. In scenarios where the relationships between modalities are highly non-linear, more complex transformations may be necessary to capture the underlying data distributions effectively.

Overfitting Risk: With a small number of additional parameters, there is a risk of overfitting, especially if the model is adapted to a limited set of modalities or tasks. This could lead to poor generalization when faced with unseen modality combinations.

Dependency on Pretrained Models: The effectiveness of the adaptation approach heavily relies on the quality of the pretrained multimodal model. If the base model is not robust or well-trained, the adaptation may not yield significant improvements.

Alternative parameter-efficient adaptation techniques that could be explored include:

Low-Rank Adaptation (LoRA): This technique involves learning low-rank updates to the weight matrices of the pretrained model, which can provide a more expressive way to adapt the model while maintaining parameter efficiency.

BitFit: This method focuses on learning only the bias terms in the model, which can be a lightweight alternative to full feature modulation while still allowing for effective adaptation.

Attention Mechanisms: Incorporating attention-based mechanisms could allow the model to focus on the most relevant features from the available modalities, potentially improving performance in scenarios with missing modalities.
By exploring these alternatives, researchers can enhance the robustness and adaptability of multimodal models in the face of missing modalities.

Beyond the tasks and datasets evaluated in this work, how generalizable is the proposed adaptation approach to other multimodal applications, such as video understanding, healthcare, or robotics, where missing modalities may be a common challenge?

The proposed adaptation approach demonstrates a high degree of generalizability to other multimodal applications, including video understanding, healthcare, and robotics, for several reasons:

Versatile Framework: The parameter-efficient adaptation framework is designed to be applicable across various multimodal tasks and datasets. Its ability to modulate intermediate features based on available modalities makes it suitable for diverse applications where different combinations of modalities may be present.

Dynamic Modality Handling: In video understanding, where frames may contain varying modalities (e.g., visual, audio, and textual information), the adaptation approach can effectively manage missing modalities by dynamically adjusting the feature extraction and fusion processes. This adaptability is crucial in real-time applications where modalities may change frequently.

Healthcare Applications: In healthcare, where data from multiple sources (e.g., imaging, clinical notes, and sensor data) are often used, the ability to adapt to missing modalities can enhance diagnostic accuracy and patient monitoring. The proposed method can be integrated into healthcare systems to improve robustness against missing data due to privacy concerns or equipment limitations.

Robotics: In robotics, where sensors may fail or provide incomplete data, the adaptation approach can help robots maintain functionality by effectively utilizing the available modalities. The ability to adapt to different sensory inputs can enhance a robot's decision-making capabilities in dynamic environments.

Transfer Learning Potential: The approach's reliance on pretrained models allows for transfer learning, making it easier to adapt to new tasks or domains with limited data. This is particularly beneficial in fields like healthcare and robotics, where labeled data may be scarce.

Overall, the proposed adaptation approach is well-positioned to address the challenges of missing modalities across a wide range of multimodal applications, making it a valuable tool for enhancing robustness and performance in real-world scenarios.