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Bilinear attention networks for visual question answering
Pytorch VQA : Visual Question Answering (https://arxiv.org/pdf/1505.00468.pdf)
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
Code for the Grounded Visual Question Answering (GVQA) model from the paper -- Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
code for Stacked attention networks for image question answering
This project is to reproduce the resulting code from the paper MUTAN: Multimodal Tucker Fusion for Visual Question Answering, and I will teach you in detail how to run through this code, including the processing of the dataset.
:question: Attention-based Visual Question Answering in Torch
Implementation of the paper "Stacked Attention Networks for Image Question Answering" in Tensorflow
Stacked attention network for answering open-ended questions about image
Pytorch implementation of VQA using Stacked Attention Networks: Multimodal architecture for image and question input, using CNN and LSTM, with stacked attention layer for improved accuracy (54.82%). Includes visualization of attention layers. Contributions welcome. Utilizes Visual VQA v2.0 dataset.
A pytroch reimplementation of "Bilinear Attention Network", "Intra- and Inter-modality Attention", "Learning Conditioned Graph Structures", "Learning to count object", "Bottom-up top-down" for Visual Question Answering 2.0
Research Code for ICCV 2019 paper "Relation-aware Graph Attention Network for Visual Question Answering"
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