Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model

• URL: http://arxiv.org/abs/2409.13407v1
• Date: Fri, 20 Sep 2024 11:13:31 GMT
• Title: Instruction-guided Multi-Granularity Segmentation and Captioning with Large Multimodal Model
• Authors: Li Zhou, Xu Yuan, Zenghui Sun, Zikun Zhou, Jingsong Lan,
• Abstract summary: We introduce a Multi-Granularity Large Multimodal Model (MGLMM) MGLMM is capable of seamlessly adjusting the granularity of Captioning (SegCap) following user instructions. It excels at tackling more than eight downstream tasks and achieves state-of-the-art performance.
• Score: 19.861556031795725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Multimodal Models (LMMs) have achieved significant progress by extending large language models. Building on this progress, the latest developments in LMMs demonstrate the ability to generate dense pixel-wise segmentation through the integration of segmentation models.Despite the innovations, the textual responses and segmentation masks of existing works remain at the instance level, showing limited ability to perform fine-grained understanding and segmentation even provided with detailed textual cues.To overcome this limitation, we introduce a Multi-Granularity Large Multimodal Model (MGLMM), which is capable of seamlessly adjusting the granularity of Segmentation and Captioning (SegCap) following user instructions, from panoptic SegCap to fine-grained SegCap. We name such a new task Multi-Granularity Segmentation and Captioning (MGSC). Observing the lack of a benchmark for model training and evaluation over the MGSC task, we establish a benchmark with aligned masks and captions in multi-granularity using our customized automated annotation pipeline. This benchmark comprises 10K images and more than 30K image-question pairs. We will release our dataset along with the implementation of our automated dataset annotation pipeline for further research.Besides, we propose a novel unified SegCap data format to unify heterogeneous segmentation datasets; it effectively facilitates learning to associate object concepts with visual features during multi-task training. Extensive experiments demonstrate that our MGLMM excels at tackling more than eight downstream tasks and achieves state-of-the-art performance in MGSC, GCG, image captioning, referring segmentation, multiple and empty segmentation, and reasoning segmentation tasks. The great performance and versatility of MGLMM underscore its potential impact on advancing multimodal research.

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