Meta-Transformer: A Unified Framework for Multimodal Learning

In this video we explain Meta-Transformer, a unified framework for multimodal learning.

With Meta-Transformer, we can use the same pre-trained transformer to process information of 12 different modalities, which is significantly more than what was possible until now with similar works such as ImageBind by Meta AI.

We review the architecture of Meta-Transformer, which is composed of Data-to-Sequence Tokenizer, a Unified Multimodal Model, and task specific models, and explain how Meta-Transformer is used to create models that can solve end tasks for different modalities.

Next we dive deeper into the pre-training process of the unified multimodal model, which is based on the LAION-2B dataset and trained using contrastive learning approach.

We finish by reviewing some of the results presented in the paper.

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Chapters:
0:00 Introducing Meta-Transformer
0:55 Meta-Transformer Architecture
3:10 Pre-training
4:46 Results

[lucamatteobarbieri]

It makes sense. Multiple modalities can be represented in the same latent space to produce a deeper understanding.

[Zale]

00:06 Meta-Transformer is a unified framework for multimodal learning that can process information from 12 different modalities.
00:32 Meta-Transformer supports a significantly wider range of data types compared to previous models.
00:58 The Meta-Transformer architecture consists of a large unified multimodal model based on transformers that can process inputs from different modalities and yield semantic embeddings.
01:27 The transformer processes information from different types of data using a data-to-sequence tokenizer, which converts inputs from different modalities to sequences of tokens.
02:22 The specialist tokenizer and end task models are trained to support specific tasks, while the larger transformer model is kept frozen and can be shared across different tasks.
03:17 The Meta-Transformer is pretrained using the LAION-2B dataset and a contrastive learning approach, where similar pairs of text and image samples are used to train the transformer to yield similar results.
04:38 The pretrained Meta-Transformer model, which was trained on texts and images, can adapt to other modalities by training the tokenizers to yield input embeddings in the same space.
05:08 Meta-Transformer achieves impressive performance on various tasks and datasets across different modalities, outperforming other models like ImageBind.
05:34 Meta-Transformer performs relatively well on text data tasks, such as the GLUE benchmark, even without a pre-trained large language model.
06:00 Meta-Transformer achieves the best results for image classification and performs well for object detection and semantic segmentation tasks.