M2ConceptBase: A Fine-grained Aligned Multi-modal Conceptual Knowledge
Base
- URL: http://arxiv.org/abs/2312.10417v1
- Date: Sat, 16 Dec 2023 11:06:11 GMT
- Title: M2ConceptBase: A Fine-grained Aligned Multi-modal Conceptual Knowledge
Base
- Authors: Zhiwei Zha, Jiaan Wang, Zhixu Li, Xiangru Zhu, Wei Song, Yanghua Xiao
- Abstract summary: We propose a multi-modal conceptual knowledge base, named M2ConceptBase, to provide fine-grained alignment between images and concepts.
Specifically, M2ConceptBase models concepts as nodes, associating each with relevant images and detailed text.
A cutting-edge large language model supplements descriptions for concepts not grounded via our symbol grounding approach.
- Score: 65.20833158693705
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large multi-modal models (LMMs) have demonstrated promising intelligence
owing to the rapid development of pre-training techniques. However, their
fine-grained cross-modal alignment ability is constrained by the coarse
alignment in image-text pairs. This limitation hinders awareness of
fine-grained concepts, resulting in sub-optimal performance. In this paper, we
propose a multi-modal conceptual knowledge base, named M2ConceptBase, which
aims to provide fine-grained alignment between images and concepts.
Specifically, M2ConceptBase models concepts as nodes, associating each with
relevant images and detailed text, thereby enhancing LMMs' cross-modal
alignment with rich conceptual knowledge. To collect concept-image and
concept-description alignments, we propose a context-aware multi-modal symbol
grounding approach that considers context information in existing large-scale
image-text pairs with respect to each concept. A cutting-edge large language
model supplements descriptions for concepts not grounded via our symbol
grounding approach. Finally, our M2ConceptBase contains more than 951K images
and 152K concepts, each associating with an average of 6.27 images and a single
detailed description. We conduct experiments on the OK-VQA task, demonstrating
that our M2ConceptBase facilitates the model in achieving state-of-the-art
performance. Moreover, we construct a comprehensive benchmark to evaluate the
concept understanding of LMMs and show that M2ConceptBase could effectively
improve LMMs' concept understanding and cross-modal alignment abilities.
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