Decoupling Common and Unique Representations for Multimodal Self-supervised Learning (2309.05300v3)

Abstract: The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning. However, most existing approaches learn only common representations across modalities while ignoring intra-modal training and modality-unique representations. We propose Decoupling Common and Unique Representations (DeCUR), a simple yet effective method for multimodal self-supervised learning. By distinguishing inter- and intra-modal embeddings through multimodal redundancy reduction, DeCUR can integrate complementary information across different modalities. We evaluate DeCUR in three common multimodal scenarios (radar-optical, RGB-elevation, and RGB-depth), and demonstrate its consistent improvement regardless of architectures and for both multimodal and modality-missing settings. With thorough experiments and comprehensive analysis, we hope this work can provide valuable insights and raise more interest in researching the hidden relationships of multimodal representations.

• The paper introduces DeCUR, which decouples common and unique features to enhance multimodal self-supervised learning.
• It employs modality-specific encoders and embedding partitioning to optimize both inter- and intra-modal training.
• Experimental results show up to a 4.8% improvement in scene classification over contrastive methods like CLIP.

DeCUR: Decoupling Common and Unique Representations for Multimodal Self-supervision

The paper introduces DeCUR, a method that advances multimodal self-supervised learning by focusing on the separation of common and unique representations in data. Multimodal self-supervised learning has increasingly gained traction due to the burgeoning availability of multi-sensor data, which offers rich potential for harnessing complementary information across different data modalities. Traditional approaches predominantly concentrate on aligning common representations across modalities, often neglecting the training of modality-specific attributes. DeCUR proposes a novel strategy to address this, providing insights into both inter- and intra-modal learning.

Methodology

DeCUR builds upon the principles of Barlow Twins, a redundancy reduction-based self-supervised learning framework. The central innovation in DeCUR is the decoupling process, which explicitly separates the common and modality-unique representations within the embedding space. This is accomplished by distinguishing dimensions dedicated to common representation from those capturing unique, modality-specific features, thereby enhancing both types during the training process.

The architecture involves:

• Modality-specific encoders and projectors: Each modality has its own network to transform input data into a high-dimensional feature space.
• Embedding partitioning: Features are divided into common and unique dimensions. DeCUR optimizes common dimensions for high correlation across modalities, while unique dimensions are decorrelated.
• Intra- and Inter-modal training: Intra-modal training ensures that the unique features maintain meaningful intra-modal characteristics, while inter-modal training focuses on aligning the common features across different modalities.

These features are optimized via a loss function that consists of terms for aligning common representations between modalities, decorrelating unique representations, and strengthening intra-modal consistency.

Experimental Evaluation

The effectiveness of DeCUR is demonstrated across three multimodal scenarios: radar-optical, RGB-elevation, and RGB-depth. Evaluations on downstream tasks, including scene classification and semantic segmentation, reveal the proposed method's utility. DeCUR delivers superior performance in scenarios with both limited and full labels, and the results are particularly notable in multimodal settings where both modalities are used.

Significantly, DeCUR-pretrained models show considerable gains when transferred to state-of-the-art supervised methods, outperforming contrastive learning techniques like SimCLR and CLIP on scene classification datasets. For instance, DeCUR improves linear classification scores by up to 4.8% over CLIP in certain scenarios.

Implications and Future Work

DeCUR addresses a key limitation in existing multimodal self-supervised learning methods by ensuring both common and unique representations are effectively captured and utilized. This approach enhances the model's capacity to understand and integrate variable modalities, which is critical in applications ranging from Earth observation to autonomous navigation systems.

The methodology holds promise for further exploration in other multimodal combinations and could inform improvements in systems requiring robust integration of diverse sensory inputs. Future research directions may include exploring alternative architectures and loss functions to further refine the delineation of common and unique features, as well as extending this framework to even more complex and higher-dimensional data sources.

The paper positions DeCUR as a critical step towards more sophisticated and insightful multimodal self-supervised learning, with potential benefits across a broad array of applications in artificial intelligence and machine learning.