VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Table 5: Comparison results of different language embeddings for VQS.
['DeconvNet (B)', 'DeconvNet (W)', 'DeconvNet (L)']
It takes about one week on one Titan X GPU machine to converge after 15 epochs. We observe that the LSTM language embedding model (L) gives rise to about 0.02 improvement over the bag-of-words (B) and word2vec embedding (W) on the challenging VQS task.