I-code V2: An Autoregressive Generation Framework Over Vision, Language, And Speech Data
Yang Ziyi, Khademi Mahmoud, Xu Yichong, Pryzant Reid, Fang Yuwei, Zhu Chenguang, Chen Dongdong, Qian Yao, Gao Mei, Chen Yi-ling, Gmyr Robert, Kanda Naoyuki, Codella Noel, Xiao Bin, Shi Yu, Yuan Lu, Yoshioka Takuya, Zeng Michael, Huang Xuedong. Arxiv 2023
[Paper]
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities. We propose closing this gap with i-Code V2, the first model capable of generating natural language from any combination of Vision, Language, and Speech data. i-Code V2 is an integrative system that leverages state-of-the-art single-modality encoders, combining their outputs with a new modality-fusing encoder in order to flexibly project combinations of modalities into a shared representational space. Next, language tokens are generated from these representations via an autoregressive decoder. The whole framework is pretrained end-to-end on a large collection of dual- and single-modality datasets using a novel text completion objective that can be generalized across arbitrary combinations of modalities. i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks, demonstrating the power of generative multimodal pretraining across a diversity of tasks and signals.
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