Key Points
- The paper proposes a method called Multimodal Pathway to improve transformers of a specific modality with irrelevant data from other modalities.
- It introduces a concrete implementation called Cross-Modal Re-parameterization, which efficiently realizes the Multimodal Pathway and brings significant and consistent performance improvements on image recognition tasks, point cloud analysis, video recognition, and audio spectrogram recognition.
- The paper highlights the limitation of existing methods, such as CLIP, which require relevant data samples from different modalities, and proposes to overcome this limitation by utilizing irrelevant data from other modalities to improve model performance.
- The proposed Multimodal Pathway framework connects components of a transformer designed for a target modality with an auxiliary transformer trained with data from another modality, allowing the processing of data from the target modality by both models.
- The study demonstrates that transformers on different modalities may have different tokenizers and main bodies, but their transformer blocks may have a similar structure.
- The paper discusses the methodology used in the Multimodal Pathway, including the concept of modality-complementary knowledge and the implementation of pathways connecting the components of different modalities to exploit this knowledge to improve model performance in a specific modality.
- The authors experimentally validate the proposed method on various modalities, including image, point cloud, video, and audio, and observe significant and consistent performance improvements compared to baseline models.
- The paper presents empirical studies confirming that the observed improvements are not solely due to a larger number of trainable parameters, but are related to the modality-complementary knowledge of sequence-to-sequence modeling in transformers.
- The study includes detailed experiments, ablation studies, and comparisons with existing methods to validate the effectiveness of the proposed Multimodal Pathway and its implementation using Cross-Modal Re-parameterization.
Summary
The paper presents a framework called Multimodal Pathway Transformer (M2PT) and a method known as Cross-Modal Re-parameterization to enhance the performance of transformers on a specific modality using irrelevant data from another modality. The proposed framework aims to improve transformers of a specific modality with irrelevant data from other modalities and utilizes the universal sequence-to-sequence modeling abilities of transformers obtained from two different modalities. The paper also conducts empirical studies on image, video, point cloud, and audio modalities, demonstrating consistent relative improvements brought by M2PT.
The proposed framework represents an early exploration in this direction, offering a novel perspective and potential as a promising approach. The paper also discusses the potential universality of learned knowledge in processing hierarchical representations across multiple modalities. Furthermore, the study investigates the impact of design choices of M2PT and provides substantial evidence of the framework's effectiveness in improving performance across various representative modalities. Despite the empirical findings, the paper acknowledges the need for further research to understand the theoretical underpinnings behind the observed improvements.
In summary, the paper makes significant contributions by proposing the M2PT framework and the Cross-Modal Re-parameterization method, demonstrating consistent performance improvements across multiple modalities, and offering a new perspective for leveraging irrelevant data from other modalities to enhance the performance of transformers on a specific modality. However, the theoretical foundations of the observed improvements remain to be further explored.
Reference: https://arxiv.org/abs/2401.14405