Viper-F1: Fast and Fine-Grained Multimodal Understanding with Cross-Modal State-Space Modulation

2025-11-14

AI Summary

Existing small-scale multimodal large language models (MLLMs) face dual challenges in resource-constrained scenarios (e.g., robotics, smart cameras): high computational overhead and poor fine-grained visual region localization, leading to suboptimal fine-grained reasoning. To address this, we propose an efficient cross-modal understanding framework. Our method replaces self-attention with liquid state-space dynamics for linear-complexity inference; introduces a Token-Grid correlation module coupled with FiLM-based conditional modulation to dynamically identify salient visual regions and enhance fine-grained localization; and designs a hybrid state-space architecture to optimize lightweight text–image patch interactions. Experiments across multiple benchmarks demonstrate substantial improvements in both efficiency and accuracy for small MLLMs—achieving fine-grained understanding performance comparable to large models while significantly reducing computational cost.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment in resource-constrained scenarios such as robotic manipulation, personal assistants, and smart cameras. Most existing methods rely on Transformer-based cross-attention, whose quadratic complexity hinders efficiency. Moreover, small vision-language models often struggle to precisely capture fine-grained, task-relevant visual regions, leading to degraded performance on fine-grained reasoning tasks that limit their effectiveness in the real world. To address these issues, we introduce Viper-F1, a Hybrid State-Space Vision-Language Model that replaces attention with efficient Liquid State-Space Dynamics. To further enhance visual grounding, we propose a Token-Grid Correlation Module, which computes lightweight correlations between text tokens and image patches and modulates the state-space dynamics via FiLM conditioning. This enables the model to selectively emphasize visual regions relevant to the textual prompt while maintaining linear-time inference. Experimental results across multiple benchmarks demonstrate that Viper-F1 achieves accurate, fine-grained understanding with significantly improved efficiency.

Problem

• Reducing computational costs of multimodal models for resource-constrained applications
• Improving fine-grained visual region capture in vision-language understanding
• Replacing quadratic-complexity attention with efficient linear-time alternatives

Innovation

• Hybrid State-Space Model replaces attention mechanisms
• Token-Grid Correlation Module enhances visual grounding
• Linear-time inference with cross-modal state-space modulation