Meta-Transformer: A Unified Framework for Multimodal Learning

[@lucamatteobarbieri2493]

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

[@Zale370]

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.

[@yochananscharf2816]

Architecture ארכיטקטורה