$M^3EL$: A Multi-task Multi-topic Dataset for Multi-modal Entity Linking

• URL: http://arxiv.org/abs/2410.18096v1
• Date: Tue, 08 Oct 2024 10:52:23 GMT
• Title: $M^3EL$: A Multi-task Multi-topic Dataset for Multi-modal Entity Linking
• Authors: Fang Wang, Shenglin Yin, Xiaoying Bai, Minghao Hu, Tianwei Yan, Yi Liang,
• Abstract summary: We propose a dataset construction pipeline and publish $M3EL$, a large-scale dataset for MEL. $M3EL$ includes 79,625 instances, covering 9 diverse multi-modal tasks, and 5 different topics. Our dataset effectively addresses these issues, and the $textitCLIP_textitND$ model fine-tuned with $M3EL$ shows a significant improvement in accuracy.
• Score: 11.334577756093923
• License:
• Abstract: Multi-modal Entity Linking (MEL) is a fundamental component for various downstream tasks. However, existing MEL datasets suffer from small scale, scarcity of topic types and limited coverage of tasks, making them incapable of effectively enhancing the entity linking capabilities of multi-modal models. To address these obstacles, we propose a dataset construction pipeline and publish $M^3EL$, a large-scale dataset for MEL. $M^3EL$ includes 79,625 instances, covering 9 diverse multi-modal tasks, and 5 different topics. In addition, to further improve the model's adaptability to multi-modal tasks, We propose a modality-augmented training strategy. Utilizing $M^3EL$ as a corpus, train the $\textit{CLIP}_{\textit{ND}}$ model based on $\textit{CLIP} (\textit{ViT}-\textit{B}-\textit{32})$, and conduct a comparative analysis with an existing multi-modal baselines. Experimental results show that the existing models perform far below expectations (ACC of 49.4%-75.8%), After analysis, it was obtained that small dataset sizes, insufficient modality task coverage, and limited topic diversity resulted in poor generalisation of multi-modal models. Our dataset effectively addresses these issues, and the $\textit{CLIP}_{\textit{ND}}$ model fine-tuned with $M^3EL$ shows a significant improvement in accuracy, with an average improvement of 9.3% to 25% across various tasks. Our dataset is available at https://anonymous.4open.science/r/M3EL.

Related papers

• Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong Baselines [63.427721165404634]
  This paper investigates an intriguing task of Multi-modal Retrieval Augmented Multi-modal Generation (M$2$RAG) This task requires foundation models to browse multi-modal web pages, with mixed text and images, and generate multi-modal responses for solving user queries. We construct a benchmark for M$2$RAG task, equipped with a suite of text-modal metrics and multi-modal metrics to analyze the capabilities of existing foundation models.
  arXiv  Detail & Related papers  (2024-11-25T13:20:19Z)
• NVLM: Open Frontier-Class Multimodal LLMs [64.00053046838225]
  We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks. We propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. We develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks.
  arXiv  Detail & Related papers  (2024-09-17T17:59:06Z)
• U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation [63.31007867379312]
  We introduce U3M: An Unbiased Multiscale Modal Fusion Model for Multimodal Semantics. We employ feature fusion at multiple scales to ensure the effective extraction and integration of both global and local features. Experimental results demonstrate that our approach achieves superior performance across multiple datasets.
  arXiv  Detail & Related papers  (2024-05-24T08:58:48Z)
• Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment [11.897888221717245]
  This paper proposes a novel CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment. Our model is simple to implement without using task-specific external knowledge, and thus can easily migrate to other multi-modal tasks.
  arXiv  Detail & Related papers  (2024-03-11T01:07:36Z)
• Toward Robust Multimodal Learning using Multimodal Foundational Models [30.755818450393637]
  We propose TRML, Toward Robust Multimodal Learning using Multimodal Foundational Models. TRML employs generated virtual modalities to replace missing modalities. We also design a semantic matching learning module to align semantic spaces generated and missing modalities.
  arXiv  Detail & Related papers  (2024-01-20T04:46:43Z)
• Lightweight In-Context Tuning for Multimodal Unified Models [57.10831399642176]
  MultiModal In-conteXt Tuning (M$2$IXT) is a lightweight module to enhance the ICL capabilities of multimodal unified models. When tuned on as little as 50K multimodal data, M$2$IXT can boost the few-shot ICL performance significantly.
  arXiv  Detail & Related papers  (2023-10-08T10:47:24Z)
• Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion [54.33764537135906]
  VideoQA Transformer models demonstrate competitive performance on standard benchmarks. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Are they achieving high scores by exploiting biases and spurious features?
  arXiv  Detail & Related papers  (2023-06-15T06:45:46Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation [27.23513712371972]
  We propose a simple yet efficient multi-modal fusion mechanism Linear Fusion. We also propose M3L: Multi-modal Teacher for Masked Modality Learning. Our proposal shows an absolute improvement of up to 10% on robust mIoU above the most competitive baselines.
  arXiv  Detail & Related papers  (2023-04-21T05:52:50Z)
• Graph Neural Networks for Multimodal Single-Cell Data Integration [32.8390339109358]
  We present a general Graph Neural Network framework $textitscMoGNN$ to tackle three tasks. textitscMoGNN$ demonstrates superior results in all three tasks compared with the state-of-the-art and conventional approaches.
  arXiv  Detail & Related papers  (2022-03-03T17:59:02Z)

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.