https://hackerpaper.kaipfister.de/p/1511.05234/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=11436587urn:hackerpapers:mention:1511.05234:11436587Wed, 06 Apr 2016 04:23:19 GMT<p>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 &quot;Most frequent 1000 answers&quot;. Source: Chen, K., Wang, J., Chen, L. C., Gao, H., Xu, W., &amp; 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…</p><p><a href="https://hackerpaper.kaipfister.de/p/1511.05234/">Paper and discussion history</a>. A mention is not an endorsement.</p>cs.CV