mlsanityData to 14 Oct 2026

PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamese

arXiv 2307.08247 ↗First posted 17 Jul 2023cs.CLcited by 3

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Abstract (arXiv)

We present in this paper a novel scheme for multimodal learning named the Parallel Attention mechanism. In addition, to take into account the advantages of grammar and context in Vietnamese, we propose the Hierarchical Linguistic Features Extractor instead of using an LSTM network to extract linguistic features. Based on these two novel modules, we introduce the Parallel Attention Transformer (PAT), achieving the best accuracy compared to all baselines on the benchmark ViVQA dataset and other SOTA methods including SAAA and MCAN.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 41 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

33 of 41 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 14 Oct)

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

3 papers.

  1. 2025LiGT: Layout-infused Generative Transformer for Visual Question Answering on Vietnamese Receiptscited by 1
  2. 2024Advancing Vietnamese Visual Question Answering with Transformer and Convolutional Integrationcited by 0
  3. 2026Fusing Visual and Textual Representations via Multi-layer Fusing Transformers for Vietnamese Visual Question Answeringcited by 0