In order to be consistent with 'human accuracies', machine accuracies are averaged over all 10 choose 9 sets of human annotators. Before evaluating machine ... |
10 янв. 2024 г. · VQA Accuracy is based on exact string matching between a candidate answer predicted by the model and a set of reference answers annotated by ... |
4 окт. 2023 г. · Abstract:8 years after the visual question answering (VQA) task was proposed, accuracy remains the primary metric for automatic evaluation. |
Visual Question Answering (VQA) on VQA v2 test-dev. View Accuracy by Date for All models. Models not using extra training data. Models using extra training ... |
1) Report test-standard accuracies, which can be calculated using either of the non-test-dev phases, i.e., "Test-Standard" or "Test-Challenge". |
Highlights · Systematic investigation of Accuracy vs. Complexity trade-off for VQA Models. · Often additional complexity does not guarantee higher VQA accuracy. |
On the most popular dataset, 'The VQA Dataset' [3], the best algorithms are now approach- ing 70% accuracy [5] (human performance is 83%). |
Visual Question Answering (VQA) - Papers With Code paperswithcode.com › task › visual-question-an...
VQA: Visual Question Answering ... Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. 21. |
Simple accuracy can be used to evaluate the multiple-choices VQA task when an algorithm gets the right answer. Comparison of the SOTA methods on VQA 2.0 dataset. |
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