mlsanityData to 14 Oct 2026

M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis

arXiv 2310.14605 ↗First posted 23 Oct 2023cs.CL cs.MMcited by 6

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Abstract (arXiv)

Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently. Existing work mainly utilizes image information to improve the performance of MABSA task. However, most of the studies overestimate the importance of images since there are many noise images unrelated to the text in the dataset, which will have a negative impact on model learning. Although some work attempts to filter low-quality noise images by setting thresholds, relying on thresholds will inevitably filter out a lot of useful image information. Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data. To achieve this goal, we borrow the idea of Curriculum Learning and propose a Multi-grained Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by adjusting the order of training data. Extensive experimental results show that our framework consistently outperforms state-of-the-art work on three sub-tasks of MABSA.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

15 of 37 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  5. 2017Mask R-CNNcited by 7,277
  6. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  7. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  8. 2019Competence-based Curriculum Learning for Neural Machine Translationcited by 202
  9. 2020Norm-Based Curriculum Learning for Neural Machine Translationcited by 60
  10. 2019Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classificationcited by 50
  11. 2018Multimodal Named Entity Recognition for Short Social Media Postscited by 43
  12. 2021Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translationcited by 41
3 more
  1. 2021RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NERcited by 37
  2. 2019Dynamically Composing Domain-Data Selection with Clean-Data Selection by "Co-Curricular Learning" for Neural Machine Translationcited by 20
  3. 2021Exploiting Curriculum Learning in Unsupervised Neural Machine Translationcited by 6

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Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Surveycited by 16
  2. 2024Recent Advances in Multimodal Affective Computing: An NLP Perspectivecited by 13
  3. 2025Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysiscited by 6
  4. 2025Hardness-Aware Dynamic Curriculum Learning for Robust Multimodal Emotion Recognition with Missing Modalitiescited by 2
  5. 2025AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysiscited by 1
  6. 2024A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysiscited by 0