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VITA: Towards Open-Source Interactive Omni Multimodal LLM

About

The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas. In this paper, we introduce VITA, the first-ever open-source Multimodal Large Language Model (MLLM) adept at simultaneous processing and analysis of Video, Image, Text, and Audio modalities, and meanwhile has an advanced multimodal interactive experience. Starting from Mixtral 8x7B as a language foundation, we expand its Chinese vocabulary followed by bilingual instruction tuning. We further endow the language model with visual and audio capabilities through two-stage multi-task learning of multimodal alignment and instruction tuning. VITA demonstrates robust foundational capabilities of multilingual, vision, and audio understanding, as evidenced by its strong performance across a range of both unimodal and multimodal benchmarks. Beyond foundational capabilities, we have made considerable progress in enhancing the natural multimodal human-computer interaction experience. VITA is the first step for the open-source community to explore the seamless integration of multimodal understanding and interaction. While there is still lots of work to be done on VITA to get close to close-source counterparts, we hope that its role as a pioneer can serve as a cornerstone for subsequent research. Project Page: https://vita-home.github.io.

Chaoyou Fu, Haojia Lin, Zuwei Long, Yunhang Shen, Yuhang Dai, Meng Zhao, Yi-Fan Zhang, Shaoqi Dong, Yangze Li, Xiong Wang, Haoyu Cao, Di Yin, Long Ma, Xiawu Zheng, Rongrong Ji, Yunsheng Wu, Ran He, Caifeng Shan, Xing Sun• 2024

Related benchmarks

TaskDatasetResultRank
Visual Question AnsweringTextVQA
Accuracy74.9
1285
Automatic Speech RecognitionLibriSpeech clean (test)
WER8.1
1156
Multimodal EvaluationMME
Score2.31e+3
658
Video Question AnsweringActivityNet-QA
Accuracy55
376
Visual Question AnsweringChartQA
Accuracy79.6
371
Visual Question AnsweringTextVQA (val)
VQA Score71.8
343
OCR EvaluationOCRBench
Score752
329
Multi-discipline Multimodal UnderstandingMMMU
Accuracy52.1
317
Mathematical ReasoningMathVista
Accuracy66.2
257
Visual Question AnsweringAI2D
Accuracy79.3
249
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