Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models

TL;DR

This paper introduces a new dataset with multi-grained concept annotations for multimodal large language models, demonstrating that integrating fine- and coarse-grained data improves multimodal understanding and generation performance.

Contribution

The paper presents MMGiC, a novel dataset with multi-grained annotations, and shows how combining different data granularities enhances MLLMs' capabilities.

Findings

Multi-grained annotations complement each other in MLLMs.

Combining MMGiC with image-caption data improves benchmark performance.

Structured multi-grained data helps MLLMs better locate and learn concepts.

Abstract

Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that integrating fine-grained concept annotations (e.g., object labels and object regions) will further improve performance, as both data granularities complement each other in terms of breadth and depth in concept representation. We introduce a new dataset featuring Multimodal Multi-Grained Concept annotations (MMGiC) for MLLMs. In constructing MMGiC, we explore the impact of different data recipes on multimodal comprehension and generation. Our analyses reveal that multi-grained concept annotations integrate and complement each other, under our structured template and a general MLLM framework. We clearly explore and demonstrate the potential of MMGiC to help MLLMs better locate and learn concepts, aligning vision and language at multiple granularities. We further validate our hypothesis by investigating the fair comparison and effective collaboration between MMGiC and image--caption data on 12 multimodal comprehension and generation benchmarks, e.g., their appropriate combination achieve 3.95% and 2.34% absolute improvements over image--caption data alone on POPE and SEED-Bench. Code, data and models will be available at https://github.com/LooperXX/MMGiC.