LLMBind: A Unified Modality-Task Integration Framework
LLMBind framework integrates multimodal tasks with dual-pathway mechanism, achieving superior performance and expandability.
Key Findings
Methodology
LLMBind employs a dual-pathway mechanism: In-Situ semantic embeddings for localization-sensitive tasks and Ex-Situ task-prompts for generation tasks. A Mixture-of-Experts (MoE) architecture routes task-specific tokens, achieving modality disentanglement. A 400k multi-turn interactive dataset supports human-like interaction.
Key Results
- On the refCOCO dataset, LLMBind achieves cIoU scores of 76.9, 78.5, and 73.2 on val, testA, and testB splits, outperforming existing methods.
- In image generation tasks, LLMBind achieves an FID score of 10.38 using GALIP baseline, surpassing NeXT-GPT and CoDi.
- In audio generation tasks, LLMBind achieves an FD of 22.90 and IS of 8.77 with CoDi baseline, outperforming AudioLDM.
Significance
LLMBind excels in multimodal task integration, addressing issues of modality interference and limited task extensibility in existing methods. Its dual-pathway mechanism and MoE architecture offer a new approach for efficient multimodal task integration, with significant impact on academia and industry.
Technical Contribution
LLMBind achieves effective multimodal task integration through a dual-pathway mechanism and MoE architecture, providing task-specific token routing and parameter isolation, significantly reducing modality interference and enhancing task extensibility.
Novelty
LLMBind is the first to apply a dual-pathway mechanism to multimodal task integration, combining In-Situ and Ex-Situ pathways for efficient integration of generation and perception tasks, differing from traditional single-modality methods.
Limitations
- In complex tasks, modality interference may still occur, affecting performance.
- High computational resource requirements may limit application in resource-constrained environments.
Future Work
Future work could explore applications in resource-constrained environments, optimize computational efficiency, and extend to more modalities and task types.
AI Executive Summary
Multimodal Large Language Models (MLLMs) face challenges in integrating pixel-level perception and high-fidelity generation tasks. Existing approaches suffer from either restricted task extensibility or performance degradation due to modality interference. LLMBind addresses this issue with a dual-pathway mechanism: In-Situ semantic embeddings for localization-sensitive tasks and Ex-Situ task-prompts for generation across image, video, and audio modalities. Additionally, a Mixture-of-Experts (MoE) architecture routes task-specific tokens, achieving modality disentanglement and mitigating negative transfer. Experiments demonstrate that LLMBind achieves excellent performance across multiple perception and generation benchmarks while maintaining superior expandability.
The core of LLMBind lies in its dual-pathway mechanism and MoE architecture. The In-Situ pathway provides fine-grained perception through semantic embeddings, while the Ex-Situ pathway offers flexible generation through task prompts. The MoE architecture ensures modality disentanglement by routing task-specific tokens, maintaining task independence and efficiency. This design enables LLMBind to flexibly extend to new generative models while preserving perception precision.
Experimental results show that LLMBind outperforms existing methods across multiple benchmark tasks, with particularly strong performance on the refCOCO dataset, achieving significantly higher cIoU scores. Additionally, LLMBind surpasses existing specialized models in image and audio generation tasks. These results indicate that LLMBind not only offers technical innovation and practicality but also provides new insights for multimodal task integration.
Deep Dive
Abstract
Despite recent progress in Multi-Modal Large Language Models (MLLMs), it remains challenging to integrate diverse tasks ranging from pixel-level perception to high-fidelity generation. Existing approaches often suffer from either restricted task extensibility or severe performance degradation due to modality interference. n this paper, we present LLMBind, an extensible framework that unifies multimodal tasks through a dual-pathway mechanism: In-Situ semantic embeddings for localization-sensitive tasks like semantic segmentation and Ex-Situ task-prompts for generation across image, video, and audio modalities. Additionally, we employ a Mixture-of-Experts (MoE) architecture to route task-specific tokens, thereby achieving modality disentanglement and mitigating negative transfer. We also curate a 400k multi-turn interactive dataset focused on iterative visual refinement to enable human-like interaction. Extensive experiments demonstrate that LLMBind achieves excellent performance across multiple perception and generation benchmarks while maintaining superior expandability.