text: 121,198,1402665891062505478,757255726553333760,Amir Feder,"👋👋New ACL paper: Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions 👋👋 With: Daniel Rosenberg, @Itai_Gat, @roireichart Asks whether VQA systems understand language. <LINK> 1/6 VQA systems seem to be constantly improving, with many different models achieving similar state-of-the-art performance. However, a more careful look reveals that they often do not understand the rich signal they are being fed with. 2/6 To understand and measure their generalization capabilities, we look at their robustness to counterfactual augmentations. These augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. 3/6 Using the augmentations, we introduce a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and counterfactual examples. 4/6 We show that RAD, unlike classical accuracy measures, can quantify when state-of-the-art systems are not robust to counterfactuals.  We find substantial failure cases which reveal that current VQA systems are still brittle.  5/6 We also connect between robustness and out-of-distribution generalization, demonstrating the predictive power of RAD for performance on unseen augmentations. 6/6",https://arxiv.org/abs/2106.04484,"Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal they are being fed with. To understand and better measure the generalization capabilities of VQA systems, we look at their robustness to counterfactually augmented data. Our proposed augmentations are designed to make a focused intervention on a specific property of the question such that the answer changes. Using these augmentations, we propose a new robustness measure, Robustness to Augmented Data (RAD), which measures the consistency of model predictions between original and augmented examples. Through extensive experimentation, we show that RAD, unlike classical accuracy measures, can quantify when state-of-the-art systems are not robust to counterfactuals. We find substantial failure cases which reveal that current VQA systems are still brittle. Finally, we connect between robustness and generalization, demonstrating the predictive power of RAD for performance on unseen augmentations. ","Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused