4M: Massively Multimodal Masked Modeling (2312.06647v1)

Abstract: Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent LLMs exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in computer vision. In this paper, we take a step in this direction and propose a multimodal training scheme called 4M. It consists of training a single unified Transformer encoder-decoder using a masked modeling objective across a wide range of input/output modalities - including text, images, geometric, and semantic modalities, as well as neural network feature maps. 4M achieves scalability by unifying the representation space of all modalities through mapping them into discrete tokens and performing multimodal masked modeling on a small randomized subset of tokens. 4M leads to models that exhibit several key capabilities: (1) they can perform a diverse set of vision tasks out of the box, (2) they excel when fine-tuned for unseen downstream tasks or new input modalities, and (3) they can function as a generative model that can be conditioned on arbitrary modalities, enabling a wide variety of expressive multimodal editing capabilities with remarkable flexibility. Through experimental analyses, we demonstrate the potential of 4M for training versatile and scalable foundation models for vision tasks, setting the stage for further exploration in multimodal learning for vision and other domains.

Overview of "4M: Massively Multimodal Masked Modeling"

The paper "4M: Massively Multimodal Masked Modeling" introduces a novel approach to develop versatile computer vision models capable of handling multiple modalities and tasks. Unlike traditional vision models that are highly specialized, the authors aim to mimic the scalability and multitask capabilities of recent LLMs by proposing a training framework that unifies various input and output modalities.

Methodology

The 4M framework utilizes a single unified Transformer encoder-decoder architecture, trained using a multimodal masked modeling objective. This approach is designed to scale across several modalities, including text, images, geometry, semantics, and neural network feature maps. The key innovation lies in representing all these diverse modalities as discrete token sequences, enabling the model to learn shared representations and predictive coding across modalities.

The training comprises a random subset of tokens as inputs and another subset as targets, allowing efficient handling of increasing modalities without excessive computational costs. This versatile training scheme supports a wide array of vision tasks out-of-the-box and facilitates effective transfer to unseen tasks and modalities. It also serves as a generative model capable of multimodal conditional generation, offering expressive editing capabilities.

Experimental Results

4M models demonstrate impressive generalization and adaptability, outperforming several baseline models across different benchmark tasks such as COCO detection, ADE20K segmentation, and NYUv2 depth estimation. Notably, while the model excels in multimodal tasks, its performance on single-modal tasks like ImageNet-1K is slightly surpassed by specialized models such as DeiT III. The paper also highlights the model's generative capabilities, showcasing its ability to perform tasks such as multimodal editing and semantic generation with high configurability using chaining and guidance techniques.

Future Implications

The 4M framework presents significant theoretical and practical implications. On a theoretical level, it helps bridge the gap between vision and LLMs, suggesting a pathway towards foundation models that can learn multimodal representations efficiently. Practically, the framework opens up avenues for developing more generalized AI systems that are not confined to specific data types or tasks.

Looking forward, future work could expand upon 4M by incorporating additional modalities, improving tokenizer quality, and utilizing larger, more diverse datasets. As AI systems continue to evolve, the pursuit of more comprehensive and unified models like 4M will be critical in overcoming current siloed approaches, ultimately leading to more advanced and versatile AI solutions.