Explainable artificial intelligence for training and tutoring. We provide two datasets with reference textual explanations to enable more research in the direction of textual explanation generation. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. share, Previous work on visual storytelling mainly focused on exploring image about images. Unfortunately, there is a dearth of datasets which include examples of how humans justify specific decisions. Note that PJ-X model then points to the vegetables, which are mentioned in the textual explanation, but not other items in the image, such as the bread. VQA Explanation Dataset (VQA-X). 11/17/2017 ∙ by Dong Huk Park, et al. 0 PJ-X is explicitly multimodal: it incorporates an explanatory attention step, which allows our model to both visually point to the evidence and justify a model decision with text. In particular, we look at cases where visual explanations are more informative than the textual explanations, and vice versa. Since the VQA-HAT annotations are collected by having humans “unblur” the images, they are more likely to introduce noise when irrelevant regions are uncovered. We aggregate all the annotations in each image and normalize them to create a probability distribution. For rank correlation, all results have standard error. Rouge: a package for automatic evaluation of summaries. Our textual justifications are able to both capture common sense and discuss specific image parts important for answering a question.

Generating reasonable explnations for correct answers is important, but it is also crucial to see how a system behaves in the face of incorrect predictions. The set contains 50% correctly answered questions. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. However, we present qualitative comparison in Figure 4. download the GitHub extension for Visual Studio. We report the percentage of generated explanations which are equivalent to or better than ground truth human explanations, when at least 2 out of 3 human judges agree.

Human visual annotations from VQA-HAT and VQA-X. 0 Details on compared models. See Figure 2 for examples of descriptions and explanations. Human attention in visual question answering: Do humans and deep networks. Proceedings of the IEEE international conference on computer We compare our model against the following baselines.
Representations (ICLR). M. G. Core, H. C. Lane, M. Van Lent, D. Gomboc, S. Solomon, and M. Rosenberg. Attentive Explanations: Justifying Decisions and Pointing to the We We ablate PJ-X and compare with related approaches on our VQA-X and ACT-X datasets through automatic and human evaluations for the generated explanations. To train and evaluate models for this task we collect two multimodal explanation datasets: Visual Question Answering Explanation (VQA-X) and Activity Explanation (ACT-X) (see  Table 1 for a summary).

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