Meta-Transformer: A Unified Framework for Multimodal Learning
(2307.10802)Abstract
Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities ($\textit{e.g.}$ natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inherent gaps among them. In this work, we propose a framework, named Meta-Transformer, that leverages a $\textbf{frozen}$ encoder to perform multimodal perception without any paired multimodal training data. In Meta-Transformer, the raw input data from various modalities are mapped into a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks, Meta-Transformer is the first framework to perform unified learning across 12 modalities with unpaired data. Experiments on different benchmarks reveal that Meta-Transformer can handle a wide range of tasks including fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series). Meta-Transformer indicates a promising future for developing unified multimodal intelligence with transformers. Code will be available at https://github.com/invictus717/MetaTransformer
Overview
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Meta-Transformer introduces a unified framework for processing and learning from diverse multimodal data using a transformer encoder.
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A common parameter space and a transformer encoder with frozen parameters extract semantic features without paired training.
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The framework includes a unified data tokenizer, modality-shared encoder, and task-specific heads for various downstream applications.
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Meta-Transformer performs well in tasks like image segmentation and audio recognition and has potential in areas like infrared and hyperspectral imaging.
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The framework's limitations include a lack of temporal and structural data handling, with future work needed in multimodal generation.
Introduction
The integration of multimodal data—ranging from natural language and 2D imagery to more complex forms like 3D point clouds and auditory information—presents a significant challenge in the development of comprehensive machine learning models. Such integration is crucial for creating systems that emulate human-level comprehension across various sensory inputs. Traditionally, architectures are tailored to specific modalities, due to the intrinsic differences that exist between data types. This paper introduces "Meta-Transformer," a novel paradigm that advances the field by providing a unified framework capable of multimodal learning across a diverse set of domains.
Unified Framework for Multimodal Learning
The core proposition of Meta-Transformer is a common parameter space, leveraging a transformer encoder with frozen parameters to process and extract semantic features from multimodal data without the need for paired training. This approach comprises three key components: a unified data tokenizer, modality-shared encoder, and tailored task-specific heads for downstream applications. The Meta-Transformer framework is notable for its ability to consistently encode 12 distinct data modalities, enabling a cohesive multimodal learning strategy.
Task-Specific Adaptation and Results
Functionality is determined through the comprehensively designed task-specific heads, which are adapted to tasks such as text classification, image segmentation, or audio recognition, to name a few. Experiments across various benchmarks exhibit the framework's broad applicability, where Meta-Transformer showcases proficiency in perception tasks, extending to practical applications in X-ray, infrared and hyperspectral imaging, IMU data analysis, as well as data mining tasks involving graphs and time-series. Notably, the framework demonstrates improved performances on a myriad of datasets, signaling a promising step towards unified models for multimodality.
Ongoing Challenges and Future Work
Despite its potential, Meta-Transformer faces challenges akin to any landmark framework. One key limitation is the framework's reduced effectiveness in capturing temporal and structural data elements that are critical for video understanding and graph representation. This underscores a potential lack of temporal and structural awareness within the current architecture. Moreover, to date, Meta-Transformer's prowess in multimodal generation remains unexplored and undefined, leaving ample space for innovation and research.
Conclusion
Meta-Transformer presents an exhilarating development within AI—exemplifying the shift towards harmonizing multimodality through shared encoding frameworks. It subtly reshapes the discussion around neural network design, moving from specificity to generality in learning across disparate datascape. As the industry gazes towards evolving AI capabilities, Meta-Transformer could redefine current pathways, offering a canvas for future generative explorations, and reasserting the indispensable role of transformers in the progression of artificial intelligence.
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