MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid

• URL: http://arxiv.org/abs/2212.14454v4
• Date: Sun, 30 Jul 2023 14:39:36 GMT
• Title: MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
• Authors: Zhuo Chen, Jiaoyan Chen, Wen Zhang, Lingbing Guo, Yin Fang, Yufeng Huang, Yichi Zhang, Yuxia Geng, Jeff Z. Pan, Wenting Song, Huajun Chen
• Abstract summary: Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs. MMEA algorithms rely on KG-level modality fusion strategies for multi-modal entity representation. This paper introduces MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid.
• Score: 40.745848169903105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images. However, current MMEA algorithms rely on KG-level modality fusion strategies for multi-modal entity representation, which ignores the variations of modality preferences of different entities, thus compromising robustness against noise in modalities such as blurry images and relations. This paper introduces MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, which dynamically predicts the mutual correlation coefficients among modalities for more fine-grained entity-level modality fusion and alignment. Experimental results demonstrate that our model not only achieves SOTA performance in multiple training scenarios, including supervised, unsupervised, iterative, and low-resource settings, but also has a limited number of parameters, efficient runtime, and interpretability. Our code is available at https://github.com/zjukg/MEAformer.

Related papers

• CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation [8.874033487493913]
  Multimodal emotion recognition in conversation aims to accurately identify emotions in conversational utterances. We propose a novel Cross-Modality Augmented Transformer with Hierarchical Variational Distillation, called CMATH, which consists of two major components. Experiments on the IEMOCAP and MELD datasets demonstrate that our proposed model outperforms previous state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-11-15T09:23:02Z)
• NativE: Multi-modal Knowledge Graph Completion in the Wild [51.80447197290866]
  We propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities. We construct a new benchmark called WildKGC with five datasets to evaluate our method.
  arXiv  Detail & Related papers  (2024-03-28T03:04:00Z)
• Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment [7.147651976133246]
  Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs for integration. We introduce a Pseudo-label Multimodal Entity Alignment (PCMEA) in a semi-supervised way. We combine momentum-based contrastive learning to make full use of the labeled and unlabeled data, which improves the quality of pseudo-label and pulls aligned entities closer.
  arXiv  Detail & Related papers  (2024-03-02T12:44:59Z)
• Model Composition for Multimodal Large Language Models [71.5729418523411]
  We propose a new paradigm through the model composition of existing MLLMs to create a new model that retains the modal understanding capabilities of each original model. Our basic implementation, NaiveMC, demonstrates the effectiveness of this paradigm by reusing modality encoders and merging LLM parameters.
  arXiv  Detail & Related papers  (2024-02-20T06:38:10Z)
• Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment [38.574204922793626]
  We present a further analysis of visual modality incompleteness, benchmarking latest MMEA models on our proposed dataset MMEA-UMVM. Our research indicates that, in the face of modality incompleteness, models succumb to overfitting the modality noise, and exhibit performance oscillations or declines at high rates of missing modality. We introduce UMAEA, a robust multi-modal entity alignment approach designed to tackle uncertainly missing and ambiguous visual modalities.
  arXiv  Detail & Related papers  (2023-07-30T12:16:49Z)
• MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing [3.3031006227198003]
  We present Modality-Agnostic Vision Transformer (MA-ViT), which aims to improve the performance of arbitrary modal attacks with the help of multi-modal data. Specifically, MA-ViT adopts the early fusion to aggregate all the available training modalities data and enables flexible testing of any given modal samples. Experiments demonstrate that the single model trained on MA-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin.
  arXiv  Detail & Related papers  (2023-04-15T13:03:44Z)
• Multi-modal Contrastive Representation Learning for Entity Alignment [57.92705405276161]
  Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs. We propose MCLEA, a Multi-modal Contrastive Learning based Entity Alignment model. In particular, MCLEA firstly learns multiple individual representations from multiple modalities, and then performs contrastive learning to jointly model intra-modal and inter-modal interactions.
  arXiv  Detail & Related papers  (2022-09-02T08:59:57Z)
• Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos [58.93586436289648]
  We propose a multi-scale cooperative multimodal transformer (MCMulT) architecture for multimodal sentiment analysis. Our model outperforms existing approaches on unaligned multimodal sequences and has strong performance on aligned multimodal sequences.
  arXiv  Detail & Related papers  (2022-06-16T07:47:57Z)
• Abstractive Sentence Summarization with Guidance of Selective Multimodal Reference [3.505062507621494]
  We propose a Multimodal Hierarchical Selective Transformer (mhsf) model that considers reciprocal relationships among modalities. We evaluate the generalism of proposed mhsf model with the pre-trained+fine-tuning and fresh training strategies.
  arXiv  Detail & Related papers  (2021-08-11T09:59:34Z)
• Attention Bottlenecks for Multimodal Fusion [90.75885715478054]
  Machine perception models are typically modality-specific and optimised for unimodal benchmarks. We introduce a novel transformer based architecture that uses fusion' for modality fusion at multiple layers. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks.
  arXiv  Detail & Related papers  (2021-06-30T22:44:12Z)

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.