arxiv_cv 98% Match Research Paper AI researchers,Developers of conversational AI,Engineers working on multimodal systems,UX designers 1 week ago

VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction

large-language-models › multimodal-llms

📄 Abstract

Abstract: Recent Multimodal Large Language Models (MLLMs) have typically focused on integrating visual and textual modalities, with less emphasis placed on the role of speech in enhancing interaction. However, speech plays a crucial role in multimodal dialogue systems, and implementing high-performance in both vision and speech tasks remains a significant challenge due to the fundamental modality differences. In this paper, we propose a carefully designed multi-stage training methodology that progressively trains LLM to understand both visual and speech information, ultimately enabling fluent vision and speech interaction. Our approach not only preserves strong vision-language capacity, but also enables efficient speech-to-speech dialogue capabilities without separate ASR and TTS modules, significantly accelerating multimodal end-to-end response speed. By comparing our method against state-of-the-art counterparts across benchmarks for image, video, and speech tasks, we demonstrate that our model is equipped with both strong visual and speech capabilities, making near real-time vision and speech interaction. Code has been released at https://github.com/VITA-MLLM/VITA.

Authors (16)

Chaoyou Fu

Haojia Lin

Xiong Wang

Yi-Fan Zhang

Yunhang Shen

Xiaoyu Liu

+10 more

Submitted

January 3, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

Introduces VITA-1.5, a framework aiming for GPT-4o level real-time vision and speech interaction. It proposes a multi-stage training methodology for LLMs to understand both visual and speech information, enabling fluent interaction and efficient speech-to-speech dialogue without separate ASR/TTS modules, significantly improving response speed.

Business Value

Revolutionizes human-computer interaction by enabling natural, real-time conversations that seamlessly integrate vision and speech, leading to more intuitive and powerful AI assistants and interfaces.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High potential for deployment in various interactive applications, provided computational resources are available. The end-to-end nature simplifies integration.

Limitations Addressed

Current MLLMs often focus on vision-language, neglecting speech's role in interaction. High-performance vision and speech tasks are challenging due to modality differences, and existing systems often have high latency due to separate ASR/TTS modules.

Performance Gains

Significantly accelerated multimodal end-to-end response speed.,Maintained strong vision-language capacity.,Enabled efficient speech-to-speech dialogue.

Technical Tags

multimodal large language modelsvision-languagespeech interactionreal-time interactionend-to-end dialogueASR-free TTSmulti-stage training

Research Topics

Multimodal AILarge Language ModelsHuman-Computer InteractionSpeech ProcessingComputer Vision

Methods & Architectures

Multi-stage training methodologyProgressive LLM trainingEnd-to-end speech-to-speech dialogue Multimodal Large Language Models (MLLMs)

Applications & Tasks

Human-Computer Interaction Virtual Assistants Customer Service Accessibility Tools Multimodal DialogueReal-time InteractionSpeech UnderstandingVision Understanding Enabling fluent vision and speech interactionReal-time multimodal dialogueSpeech-to-speech dialogue without separate ASR/TTS

Datasets & Benchmarks

Benchmarks

Benchmarks for image, video, and speech tasks

Response speedAccuracy on vision tasksPerformance on speech tasks

Related Fields

Artificial IntelligenceNatural Language ProcessingSpeech TechnologyComputer Vision

Keywords

multimodal LLMVITA-1.5GPT-4oreal-time interactionvision-speechdialogue systemsASR-freeTTSend-to-endmulti-stage training

Academic Context

#Multimodal AI#Large Language Models#Human-Computer Interaction#Speech Processing#Computer Vision

Companies & Organizations

Companies Mentioned

OpenAI

Commercial Potential

Potential Products

Next-generation virtual assistantsInteractive AI tutorsAdvanced customer support botsReal-time translation and communication tools

Target Industries

TechnologyCustomer ServiceEducationHealthcareEntertainment

Use Case Examples

A virtual assistant that can see an object, hear a question about it, and respond verbally.Real-time collaborative tools where users can interact using both speech and visual input.Accessibility tools for individuals with communication or visual impairments.

Competitive Edge

Aims to match or exceed state-of-the-art multimodal capabilities (like GPT-4o) with a focus on real-time, integrated speech and vision interaction.

Market Opportunity

Massive and rapidly growing market for AI-powered conversational interfaces and multimodal applications.

Revenue Models

API access feeslicensing of the model/platformintegration into subscription services.

Resource Requirements

Compute Needs

Very high, typical for training large multimodal models.

Data Requirements

Requires large-scale, diverse datasets covering vision, speech, and text, aligned for multimodal interaction.

Deployment Constraints

Requires significant computational resources for real-time inference; latency is critical.

Scalability

Scalability to handle numerous concurrent users and complex interactions is a key challenge.

Regulatory Considerations

Data privacyethical use of AI in communicationpotential bias in multimodal understanding.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years for widespread commercial adoption.

Patent Potential

High, for the novel multi-stage training methodology and end-to-end speech-vision integration.

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