Many Heads but One Brain: Fusion Brain -- a Competition and a Single Multimodal Multitask Architecture
This addresses the challenge of creating unified AI systems for multimodal tasks, though it is incremental as it builds on existing foundation models.
The authors introduced the Fusion Brain competition to develop a universal architecture capable of processing images, texts, and code for multiple tasks, and proposed a baseline multimodal multitask architecture that is competitive and more energy-efficient than task-specific models.
Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language. The Fusion Brain Challenge combines the following specific tasks: Code2code Translation, Handwritten Text recognition, Zero-shot Object Detection, and Visual Question Answering. We have created datasets for each task to test the participants' submissions on it. Moreover, we have collected and made publicly available a new handwritten dataset in both English and Russian, which consists of 94,128 pairs of images and texts. We also propose a multimodal and multitask architecture - a baseline solution, in the center of which is a frozen foundation model and which has been trained in Fusion mode along with Single-task mode. The proposed Fusion approach proves to be competitive and more energy-efficient compared to the task-specific one.