LLMBind: A Unified Modality-Task Integration Framework
Core Concepts
LLMBind introduces a unified framework for integrating modality tasks, showcasing promising results in various multimodal tasks.
Abstract
LLMBind proposes a unified framework for integrating Large Language Models with task-specific tokens to handle diverse multimodal tasks efficiently. The model demonstrates effectiveness across image, video, audio generation, segmentation, and editing tasks. By introducing a Mixture-of-Experts technique and creating a multi-task dataset, LLMBind shows potential in advancing AI agent modeling for universal modalities. The study also explores related works in cross-modal understanding, generation, and editing, highlighting the significance of the proposed framework.
LLMBind
Stats
LLMBind achieves referring segmentation scores of 76.9 (val), 78.5 (testA), and 73.2 (testB) on the refCOCO dataset.
In text-to-audio generation, LLMBind outperforms models like NeXT-GPT with an FD score of 22.90 and an IS score of 8.77 on the AudioCaps dataset.
For text-to-video generation, LLMBind achieves an FID score of 11.09 on the MSR-VTT dataset.
LLMBind attains an FID score of 11.21 in text-to-image generation on the COCO-caption dataset.
In reasoning segmentation evaluation, LLMBind surpasses LISA-7B with GIoU and CIoU scores of 62.4 and 66.9.
Quotes
"LLMBind showcases promising results in advancing human-like MLLM and AI agents."
"Our framework can be easily extended to other modality tasks."
"LLMBind efficiently integrates various modalities through task-specific tokens."