Understanding and explaining deep learning models is an imperative task.
Towards this, we propose a method that obtains gradient-based certainty
estimates that also provide visual attention maps. Particularly, we solve for
visual question answering task. We incorporate modern probabilistic deep
learning methods that we further improve by using the gradients for these
estimates. These have two-fold benefits: a) improvement in obtaining the
certainty estimates that correlate better with misclassified samples and b)
improved attention maps that provide state-of-the-art results in terms of
correlation with human attention regions. The improved attention maps result in
consistent improvement for various methods for visual question answering.
Therefore, the proposed technique can be thought of as a recipe for obtaining
improved certainty estimates and explanations for deep learning models. We
provide detailed empirical analysis for the visual question answering task on
all standard benchmarks and comparison with state of the art methods.

这篇研究提出了一种方法，通过基于梯度的确定性估计来解决视觉问答任务，进一步利用这些估计来获得可视化的注意力图，从而提高深度学习模型的确定性估计和解释能力，并在各项标准基准测试中提供实证分析及与现有方法的比较

基于不确定性的视觉问答类激活图

Uncertainty based Class Activation Maps for Visual Question Answering

Understanding and explaining deep learning models is an imperative task.
Towards this, we propose a method that obtains gradient-based certainty
estimates that also provide visual attention maps. Particularly, we solve for
visual question answering task. We incorporate modern probabilistic deep
learning methods that we further improve by using the gradients for these
estimates. These have two-fold benefits: a) improvement in obtaining the
certainty estimates that correlate better with misclassified samples and b)
improved attention maps that provide state-of-the-art results in terms of
correlation with human attention regions. The improved attention maps result in
consistent improvement for various methods for visual question answering.
Therefore, the proposed technique can be thought of as a recipe for obtaining
improved certainty estimates and explanation for deep learning models. We
provide detailed empirical analysis for the visual question answering task on
all standard benchmarks and comparison with state of the art methods.

本研究针对深度学习模型的解释提出了一种方法，通过梯度获取与误分类样本更相关的确信度估计和提供视觉注意力图，特别是在视觉问答任务中，该方法采用现代概率深度学习方法，并进一步通过这些估计梯度进行改进，得到具有人类注意区域相关性的先进的注意力图，从而提供了更好的解释能力。我们在所有标准基准测试上提供了详细的实证分析和与现有方法的比较。