PAT: Parallel Attention Transformer for Visual Question Answering in Vietnamese
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Question AnsweringVietnamese Visual Question AnsweringVisual Question AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering
In recent years, multi-modal transformers have shown significant progress in Vision-Language tasks, such as Visual Question Answering (VQA), outperforming previous architectures by a considerable margin. This improvement…
POSQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering
Attention mechanism has gained huge popularity due to its effectiveness in achieving high accuracy in different domains. But attention is opportunistic and is not justified by the content or usability of the content. Tra…
DiversityQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)+1ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention
Textbook Question Answering is a complex task in the intersection of Machine Comprehension and Visual Question Answering that requires reasoning with multimodal information from text and diagrams. For the first time, thi…
Multiple-choiceQuestion AnsweringReading ComprehensionVisual Question Answering+1ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention
Textbook Question Answering is a complex task in the intersection of Machine Comprehension and Visual Question Answering that requires reasoning with multimodal information from text and diagrams. For the first time, thi…
Multiple-choiceQuestion AnsweringReading ComprehensionVisual Question Answering+1Multilevel Hierarchical Network with Multiscale Sampling for Video Question Answering
Video question answering (VideoQA) is challenging given its multimodal combination of visual understanding and natural language processing. While most existing approaches ignore the visual appearance-motion information a…
multimodal interactionQuestion AnsweringVideo Question AnsweringVisual Reasoning