Nexus-O: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision

• URL: http://arxiv.org/abs/2503.01879v2
• Date: Fri, 07 Mar 2025 09:21:40 GMT
• Title: Nexus-O: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision
• Authors: Che Liu, Yingji Zhang, Dong Zhang, Weijie Zhang, Chenggong Gong, Haohan Li, Yu Lu, Shilin Zhou, Yue Lu, Ziliang Gan, Ziao Wang, Junwei Liao, Haipang Wu, Ji Liu, André Freitas, Qifan Wang, Zenglin Xu, Rongjuncheng Zhang, Yong Dai,
• Abstract summary: We introduce textbfNexus-O, an industry-level textbfomni-perceptive and -interactive model capable of efficiently processing Audio, Image, Video, and Text data.<n>We address three key research questions: First, how can models be efficiently designed and trained to achieve tri-modal alignment, understanding and reasoning capabilities across multiple modalities?<n>Second, what approaches can be implemented to evaluate tri-modal model robustness, ensuring reliable performance and applicability in real-world scenarios?<n>Third, what strategies can be employed to curate and obtain high-quality, real-life scenario
• Score: 50.23246260804145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human beings perceive the real world through a spectrum of sensory modalities, encompassing auditory, visual, and linguistic faculties. The journey towards achieving Artificial General Intelligence (AGI) necessitates the development of models that can emulate these multifaceted perceptual capabilities and comprehensively understand these diversified data. To this end, we introduce \textbf{Nexus-O}, an industry-level \textbf{omni-perceptive and -interactive} model capable of efficiently processing Audio, Image, Video, and Text data in any combination and output audio/text in an end-to-end way. We systematically investigate Nexus-O by addressing three key research questions: First, how can models be efficiently designed and trained to achieve tri-modal alignment, understanding and reasoning capabilities across multiple modalities? Second, what approaches can be implemented to evaluate tri-modal model robustness, ensuring reliable performance and applicability in real-world scenarios? Third, what strategies can be employed to curate and obtain high-quality, real-life scenario speech datasets? For the first question, we design and pre-train Nexus-O based on the vision-language model, rather than the language model. By pre-training the model over high-quality synthetic audio data, our model is capable of tri-modal perception and interaction. For the second question, we introduce a new audio testbed, Nexus-O-audio, comprising diverse Automatic Speech Recognition (ASR) samples, spanning various real-world scenarios, such as corporate meetings and live stream. For the third question, we design the speech data synthesis pipeline to obtain high-quality speech training datasets, covering various real-world scenarios. Comprehensive experimentation and an in-depth analysis of tri-modal alignment over latent space demonstrate the advantages of our model on downstream tasks.

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