Features of a CNN overlap, reducing this leads to better generalization
arxiv.org2 pointsby iamaaditya0 comments
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.] 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". 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. Xu, Huijuan, and Kate Saenko. "Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering." arXiv preprint arXiv:1511.05234(2015).
However, I think my words were sloppy, as I could not find more concrete
proof in the literature, but I will revisit them with detail to recollect
where I read about RNN generating answers not overachieveing softmax
classification over Top K distribution of answers.