mlsanityData to 14 Oct 2026

BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering

arXiv 2307.15335 ↗First posted 28 Jul 2023cs.CL cs.CVcited by 5

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Abstract (arXiv)

Visual Question Answering (VQA) is an intricate and demanding task that integrates natural language processing (NLP) and computer vision (CV), capturing the interest of researchers. The English language, renowned for its wealth of resources, has witnessed notable advancements in both datasets and models designed for VQA. However, there is a lack of models that target specific countries such as Vietnam. To address this limitation, we introduce a transformer-based Vietnamese model named BARTPhoBEiT. This model includes pre-trained Sequence-to-Sequence and bidirectional encoder representation from Image Transformers in Vietnamese and evaluates Vietnamese VQA datasets. Experimental results demonstrate that our proposed model outperforms the strong baseline and improves the state-of-the-art in six metrics: Accuracy, Precision, Recall, F1-score, WUPS 0.0, and WUPS 0.9.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 24 of its 28 references that are in the map. First posted on arXiv 28 Jul 2023.

Builds on

24 of 28 references.

References · 24

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  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
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12 more
  1. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,230
  2. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
  3. 2021Vector-quantized Image Modeling with Improved VQGANcited by 658
  4. 2022Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Taskscited by 511
  5. 2021VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Expertscited by 425
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  12. 2021BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamesecited by 34

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 2.

5 papers.

  1. 2023ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamesecited by 5
  2. 2025LiGT: Layout-infused Generative Transformer for Visual Question Answering on Vietnamese Receiptscited by 1
  3. 2024Advancing Vietnamese Visual Question Answering with Transformer and Convolutional Integrationcited by 0
  4. 2026AutoViVQA: A Large-Scale Automatically Constructed Dataset for Vietnamese Visual Question Answeringcited by 0
  5. 2026Fusing Visual and Textual Representations via Multi-layer Fusing Transformers for Vietnamese Visual Question Answeringcited by 0