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An Improved Attention for Visual Question Answering
We consider the problem of Visual Question Answering (VQA). Given an ima...
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MCQA: Multimodal Co-attention Based Network for Question Answering
We present MCQA, a learning-based algorithm for multimodal question answ...
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Holistic Multi-modal Memory Network for Movie Question Answering
Answering questions according to multi-modal context is a challenging pr...
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Regularizing Attention Networks for Anomaly Detection in Visual Question Answering
For stability and reliability of real-world applications, the robustness...
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Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
Visual question answering (VQA) is of significant interest due to its po...
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Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based VisualQuestion Answering
Fact-based Visual Question Answering (FVQA) requires external knowledge ...
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Decision problems for Clark-congruential languages
A common question when studying a class of context-free grammars is whet...
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Answer-checking in Context: A Multi-modal FullyAttention Network for Visual Question Answering
Visual Question Answering (VQA) is challenging due to the complex cross-modal relations. It has received extensive attention from the research community. From the human perspective, to answer a visual question, one needs to read the question and then refer to the image to generate an answer. This answer will then be checked against the question and image again for the final confirmation. In this paper, we mimic this process and propose a fully attention based VQA architecture. Moreover, an answer-checking module is proposed to perform a unified attention on the jointly answer, question and image representation to update the answer. This mimics the human answer checking process to consider the answer in the context. With answer-checking modules and transferred BERT layers, our model achieves the state-of-the-art accuracy 71.57% using fewer parameters on VQA-v2.0 test-standard split.
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