Data augmentation to improve robustness of image captioning solutions. In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution, and notice a degradation in solution performance as blur intensity increases.
Jun 10, 2021
We investigate techniques to improve the robustness of the solution to motion blur using training data augmentation at each or both stages of the solution, i.e.�...
Jun 10, 2021 � In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image�...
In this paper, we study the impact of motion blur, a common quality flaw in real world images, on a state-of-the-art two-stage image captioning solution,�...
Aug 11, 2024 � Neural image classifiers can often learn to make predictions by overly relying on non- predictive features that are spuriously corre-.
Jun 7, 2024 � Being widely used in learning unbiased visual question answering (VQA) models, Data Augmentation (DA) helps mitigate language biases by�...
In this work, we come up with a novel data augmentation technique using text-to-text and text-to-image generative models to create good-quality augmented�...
Jul 5, 2024 � Data augmentation enhances the robustness of the model by generating synthetic data or modifying existing data. Techniques like rotation,�...
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Mar 27, 2024 � Through this evaluation, we aim to demonstrate that RRDA can significantly improve the interpretability and reliability of image classification�...
Nov 9, 2021 � In this paper, we focus on reducing robust overfitting by using common data augmentation schemes. We demonstrate that, contrary to previous�...