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womengfeixiang's Projects

ban-vqa icon ban-vqa

Bilinear attention networks for visual question answering

basic_vqa icon basic_vqa

Pytorch VQA : Visual Question Answering (https://arxiv.org/pdf/1505.00468.pdf)

deeplearning-500-questions icon deeplearning-500-questions

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

gvqa icon gvqa

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

imageqa-san icon imageqa-san

code for Stacked attention networks for image question answering

mutan-for-vqa icon mutan-for-vqa

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.

visual-question-answering-using-stacked-attention-networks icon visual-question-answering-using-stacked-attention-networks

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.

vqa2.0-recent-approachs-2018.pytorch icon vqa2.0-recent-approachs-2018.pytorch

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

vqa_regat icon vqa_regat

Research Code for ICCV 2019 paper "Relation-aware Graph Attention Network for Visual Question Answering"

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