منابع مشابه
C-VQA: A Compositional Split of the Visual Question Answering (VQA) v1.0 Dataset
Visual Question Answering (VQA) has received a lot of attention over the past couple of years. A number of deep learning models have been proposed for this task. However, it has been shown [1–4] that these models are heavily driven by superficial correlations in the training data and lack compositionality – the ability to answer questions about unseen compositions of seen concepts. This composi...
متن کاملAttention on Attention: Architectures for Visual Question Answering (VQA)
Visual Question Answering (VQA) is an increasingly popular topic in deep learning research, requiring coordination of natural language processing and computer vision modules into a single architecture. We build upon the model which placed first in the VQA Challenge by developing thirteen new attention mechanisms and introducing a simplified classifier. We performed 300 GPU hours of extensive hy...
متن کاملInverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool
In recent years, visual question answering (VQA) has become topical. The premise of VQA’s significance as a benchmark in AI, is that both the image and textual question need to be well understood and mutually grounded in order to infer the correct answer. However, current VQA models perhaps ‘understand’ less than initially hoped, and instead master the easier task of exploiting cues given away ...
متن کاملInvestigating Embedded Question Reuse in Question Answering
The investigation presented in this paper is a novel method in question answering (QA) that enables a QA system to gain performance through reuse of information in the answer to one question to answer another related question. Our analysis shows that a pair of question in a general open domain QA can have embedding relation through their mentions of noun phrase expressions. We present methods f...
متن کاملRevisiting Visual Question Answering Baselines
Visual question answering (VQA) is an interesting learning setting for evaluating the abilities and shortcomings of current systems for image understanding. Many of the recently proposed VQA systems include attention or memory mechanisms designed to support “reasoning”. For multiple-choice VQA, nearly all of these systems train a multi-class classifier on image and question features to predict ...
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ژورنال
عنوان ژورنال: International Journal of Computer Vision
سال: 2016
ISSN: 0920-5691,1573-1405
DOI: 10.1007/s11263-016-0966-6