ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering · HackerPapers discussions

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering — mentioned by iamaaditya

Wed, 06 Apr 2016 04:35:03 GMT

hi syllogism Right now everyone doing this is highly focussed on the competition and trying to beat the numbers. For that purpose certainly they would want to stick to predicting Top K answers. For e.g see this table Model Q+I [1] Q+I+C [1] ATT 1000 ATT Full ACC. 0.2678 0.2939 0.4838 0.4651 Where ATT full is when using all the words, it performs worse than ATT 1000 (Source)[Chen, K., Wang, J., Chen, L. C., Gao, H., Xu, W., & Nevatia, R. (2015). ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering. arXiv preprint arXiv:1511.05960.] Once the competition is over…

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ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering — mentioned by iamaaditya

Wed, 06 Apr 2016 04:23:19 GMT

1. Model Q+I [1] Q+I+C [1] ATT 1000 ATT Full ACC. 0.2678 0.2939 0.4838 0.4651 Where ATT Full represents using all the words in the vocabulary, as you can see it performs worse than "Most frequent 1000 answers". Source: Chen, K., Wang, J., Chen, L. C., Gao, H., Xu, W., & Nevatia, R. (2015). ABC- CNN: An Attention Based Convolutional Neural Network for Visual Question Answering. arXiv preprint arXiv:1511.05960. 2. (a) Several early papers about VQA directly adapt the image captioning models to solve the VQA problem [10][11] by generating the answer using a recurrent LSTM network conditioned on…

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