Fusing Visual and Textual Representations via Multi-layer Fusing Transformers for Vietnamese Visual Question Answering

arXiv:2610.01637 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Transformer — 27× in this paper · explained in 2 episodes
• Visual Question Answering — 18× in this paper
• Attention Mechanism — 6× in this paper · explained in 2 episodes
• Multi-layer Fusing Transformer — 6× in this paper
• F1 Score — 5× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Parallel Attention Transformer — 2× in this paper
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Conference on Multimedia Analysis and Pattern Recognition — 1× in this paper
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Multi-vision Contextual Attention — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• BEiT: BERT Pre-Training of Image Transformers
• EVJVQA Challenge: Multilingual Visual Question Answering
• BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
• What's so special about BERT's layers? A closer look at the NLP pipeline in monolingual and multilingual models

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