  Multimodal knowledge bases (MMKBs) provide cross-modal aligned knowledge
crucial for multimodal tasks. However, the images in existing MMKBs are
generally collected for entities in encyclopedia knowledge graphs. Therefore,
detailed groundings of visual semantics with linguistic concepts are lacking,
which are essential for the visual concept cognition ability of multimodal
models. Addressing this gap, we introduce M^2ConceptBase, the first
concept-centric MMKB. M^2ConceptBase models concepts as nodes with associated
images and detailed textual descriptions. We propose a context-aware multimodal
symbol grounding approach to align concept-image and concept-description pairs
using context information from image-text datasets. Comprising 951K images and
152K concepts, M^2ConceptBase links each concept to an average of 6.27 images
and a single description, ensuring comprehensive visual and textual semantics.
Human studies confirm more than 95% alignment accuracy, underscoring its
quality. Additionally, our experiments demonstrate that M^2ConceptBase
significantly enhances VQA model performance on the OK-VQA task. M^2ConceptBase
also substantially improves the fine-grained concept understanding capabilities
of multimodal large language models through retrieval augmentation in two
concept-related tasks, highlighting its value.
