Learning Visual Knowledge Memory Networks for Visual Question Answering
Table 2: Results comparison with state-of-the-art methods on the VQA v1.0 dataset. “-” indicates the result is not available. “Att” indicates some attention mechanism is used. “CoAtt” means co-attention. “GloVec” indicates that the word embedding method [33] is adopted. Here all the reported results are obtained with a single model without model ensemble. For each answer-type, the best result is bolded.
['Model', 'test-dev Open-Ended', 'test-dev Open-Ended', 'test-dev Open-Ended', 'test-dev Open-Ended', 'test-dev MC', 'test-standard Open-Ended', 'test-standard Open-Ended', 'test-standard Open-Ended', 'test-standard Open-Ended', 'test-standard MC']
For easy and fair comparison, we also list the results by state-of-the-art methods with single model. It shows that VKMN outperforms state-of-the-art results on both Open-Ended and Multiple-choice tasks, and especially for the “other” answer-type, which proves that VKMN is effective on incorporating external knowledge for answering 6W questions (what, where, when, who, why, and how). It is obvious that the VKMN model could attend to highly relevant knowledge triples. These cases are related to spatial relationship reasoning, in which MLB does not get correct attention regions.