Table 4 indicates that explanations are better than no explanations and our model is more helpful than models trained on descriptions and also models trained to generate textual explanations only. Measuring machine intelligence through visual question answering. To do this, we consider the annotations collected in [40] which say how old a human must be to answer a question. Furthermore, we qualitatively demonstrated that our model is able to point to the evidence as well as to give natural sentence justifications, similar to ones humans give. Recognition, Attentive Explanations: Justifying Decisions and Pointing to the An overview of our model is presented in Figure 5. Many questions in VQA are of the sort: “What is the color of the banana?”. Reconstructive expert system explanation. Interestingly, in Figure 8, we present some examples where visual pointing is more insightful than textual justification, and vice versa. However, the concept is easily conveyed when looking at the visual pointing result. (CVPR). DH. The distribution is then visualized over the image as a heatmap. Conference on Computer Vision and Pattern Recognition Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell, M. Rohrbach, Multimodal Explanations: Justifying Decisions and Pointing to the Evidence. S. Bell, P. Upchurch, N. Snavely, and K. Bala. In contrast, we consider a more realistic scenario and do not make any assumptions about where the activity occurs at test time. Previous work on visual storytelling mainly focused on exploring image Justification narratives for individual classifications. “Ours” significantly outperforms “Ours with Descriptions” by a large margin on both datasets which is expected as descriptions are insufficient for the task of generating explanations. Park, LA. For computing Rank Correlation, we follow [11] where we scale the generated attention map and the human ground-truth annotations from the VQA-X/ACT-X/VQA-HAT datasets to 14×14, rank the pixel values, and then compute correlation between these two ranked lists. ∙ This process gives us a. Evidence, Interpretable and Fine-Grained Visual Explanations for Convolutional V. Escorcia, J. C. Niebles, and B. Ghanem. We deliberately design our Pointing and Justification Model (PJ-X) to allow training these two tasks. 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. We encode the question Q with a 2-layer LSTM, which we refer to as fQ(Q). Here, we detail our experimental setup in terms of model training, hyperparameter settings, and evaluation metrics. For example in the top row of. global context, e.g. The command will save generated textual and visual explanations in the directory designated by --our_dir . It is difficult for humans to explain answers to such questions because it requires explaining a fundamental visual property: color. On the relationship between visual attributes and convolutional 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). After unzipping the datasets, symlink it to PJ-X-ACT/ACT-X and place the data accordingly so that the file sructure looks as the following: We use pretrained VQA model (using VQA training set) for the explanation task. “Ours on Descriptions” performs worse on certain metrics compared to [17] which may be attributed to additional training signals generated from discriminative loss and policy gradients, but further investigation is left for future work. For example, given the question “What is the name of the restaurant?”, human gaze might capture other buildings before settling on the restaurant. In Figure 9, we can see that the explanations are consistent with the incorrectly predicted answer for both VQA-X and ACT-X. MHP dataset also comes with sentence descriptions provided by [27]. We provide two datasets with reference textual explanations to enable more research in the direction of textual explanation generation. we present quantitative results on ablations done for textual justification and visual pointing tasks, and discuss their implications. Applying 1×1 convolutions, element-wise multiplication followed by signed square-root, L2 normalization, and Dropout, results in a multimodal feature.
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