Multimodal Pathway Transformer: Expanding the Versatility of Transformers

Transformers: Enhancing Performance through Multimodal Pathway

Transformers have found diverse applications in text classification, map construction, object detection, point cloud analysis, and audio spectrogram recognition. They are widely used, but continued success raises questions about improvement. A group of researchers is looking into this potential in “Multimodal Pathway Transformers”. The researchers seek to enhance transformers designed for specific modalities, such as ImageNet, by incorporating irrelevant data from unrelated modalities like audio and point cloud datasets.

M2PT connects transformers of different modalities in an innovative way. The results demonstrate substantial and consistent performance improvements across image, point cloud, video, and audio recognition tasks. If you want to learn more, check out the Paper and Github.

The goal is to build models that can utilize the universal sequence-to-sequence modeling capabilities of transformers from multiple modalities. Such an approach distinguishes M2PT from others that rely on paired or interleaved data from different modalities. The researchers believe that incorporating irrelevant data from other modalities can lead to substantial performance improvements across different recognition tasks.

In conclusion, the paper introduces the Multimodal Pathway to enhance transformer performance on a specific modality by incorporating irrelevant data from other modalities. The researchers present Cross-Modal Re-parameterization as a tangible implementation, enabling the utilization of auxiliary weights without incurring inference costs. Experimental results consistently show substantial performance improvements across image, point cloud, video, and audio recognition tasks, emphasizing the efficacy of leveraging irrelevant data from diverse modalities in transformer-based models.

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