Multimodal Reasoning with Multimodal Knowledge Graph

• URL: http://arxiv.org/abs/2406.02030v2
• Date: Wed, 5 Jun 2024 03:28:01 GMT
• Title: Multimodal Reasoning with Multimodal Knowledge Graph
• Authors: Junlin Lee, Yequan Wang, Jing Li, Min Zhang,
• Abstract summary: Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge. We propose the Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method to learn rich and semantic knowledge across modalities.
• Score: 19.899398342533722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal reasoning with large language models (LLMs) often suffers from hallucinations and the presence of deficient or outdated knowledge within LLMs. Some approaches have sought to mitigate these issues by employing textual knowledge graphs, but their singular modality of knowledge limits comprehensive cross-modal understanding. In this paper, we propose the Multimodal Reasoning with Multimodal Knowledge Graph (MR-MKG) method, which leverages multimodal knowledge graphs (MMKGs) to learn rich and semantic knowledge across modalities, significantly enhancing the multimodal reasoning capabilities of LLMs. In particular, a relation graph attention network is utilized for encoding MMKGs and a cross-modal alignment module is designed for optimizing image-text alignment. A MMKG-grounded dataset is constructed to equip LLMs with initial expertise in multimodal reasoning through pretraining. Remarkably, MR-MKG achieves superior performance while training on only a small fraction of parameters, approximately 2.25% of the LLM's parameter size. Experimental results on multimodal question answering and multimodal analogy reasoning tasks demonstrate that our MR-MKG method outperforms previous state-of-the-art models.

Related papers

• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning [51.80447197290866]
  Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies. We introduce a novel framework with Mixture of Modality Knowledge experts (MoMoK) to learn adaptive multi-modal entity representations.
  arXiv  Detail & Related papers  (2024-05-27T06:36:17Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [60.17448025069594]
  We investigate the potential of Large Language Models to enhance multimodal representation in multimodal item-to-item recommendations. One feasible method is the transfer of Multimodal Large Language Models (MLLMs) for representation tasks. We propose a novel training framework, NoteLLM-2, specifically designed for multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts [54.529880848937104]
  We develop a unified MLLM with the MoE architecture, named Uni-MoE, that can handle a wide array of modalities. Specifically, it features modality-specific encoders with connectors for a unified multimodal representation. We evaluate the instruction-tuned Uni-MoE on a comprehensive set of multimodal datasets.
  arXiv  Detail & Related papers  (2024-05-18T12:16:01Z)
• What Makes Multimodal In-Context Learning Work? [58.48612721156335]
  We present a framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. We identify several biases and limitations of M-ICL that warrant consideration prior to deployment.
  arXiv  Detail & Related papers  (2024-04-24T08:50:45Z)
• Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion [70.9767518332692]
  Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. We propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion.
  arXiv  Detail & Related papers  (2024-02-19T14:59:07Z)
• MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning [40.60328470622483]
  We propose a novel model entitled MMKGR (Multi-hop Multi-modal Knowledge Graph Reasoning) The model contains the following two components: (1) a unified gate-attention network which is designed to generate effective multi-modal complementary features through sufficient attention interaction and noise reduction; and (2) a complementary feature-aware reinforcement learning method which is proposed to predict missing elements by performing the multi-hop reasoning process. The experimental results demonstrate that MMKGR outperforms the state-of-the-art approaches in the MKG reasoning task.
  arXiv  Detail & Related papers  (2022-09-03T13:07:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.