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在两个数据集上进行实验:VQA2和CLEVR。 对这两个数据集进行了过滤,去掉了yes/no、number类型的样本,因为对于VQG任务,几乎不可能根据yes这个回答来生成满意的question,yes/no、number类型的回答缺乏足够的信息量(less informative answers)。 评价指标:top-1 accuracy、top-5 accuracy(VQA),CIDEr(VQG) 一些实验表格...
Visual Question Answering (VQA) is the task of taking as input an image and afree-form natural language question about the image, and producing an accurateanswer. In this work we view VQA as a "feature extraction" module to extractimage and caption representations. We employ these representation...
Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local ...
Visual Question Answering (VQA) is the problem of automatically answering a natural language question about a given image or video. Standard Arabic is the
A computer-implemented method for predicting answers to questions concerning medical image analytics reports includes splitting a medical image analytics report into a plurality of sentences and generating a plurality of sentence embedding vectors by applying a natural language processing framework to the ...
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WithQAnything, you can simply drop any locally stored file of any format and receive accurate, fast, and reliable answers. Currently supported formats include:PDF(pdf),Word(docx),PPT(pptx),XLS(xlsx),Markdown(md),Email(eml),TXT(txt),Image(jpg,jpeg,png),CSV(csv),Web links(html)and more...
detects a question event, that time window of high gamma activity is passed to a question classifier that uses phone-level Viterbi decoding to compute question utterance likelihoods.fThe question with the highest likelihood is output as the decoded question.gTo integrate questions and answers, the ...