U-CAM: Visual Explanation Using Uncertainty Based Class Activation Maps

By Badri N. Patro, Mayank Lunayach, Shivansh Patel, Vinay P. Namboodiri. 2019

84 citations recorded by the source corpus.

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.

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Citations in the corpus

LayerCAM: Exploring Hierarchical Class Activation Maps for Localization (2021, methodology): “Researchers have proposed many attention methods [11], [12], [13], [14], [15] to locate the object regions of the class of interest from the powerful CNN based image classiﬁers [16], [17], [18], [19], [20], [21], [22].”
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020, methodology): “[228] devised gradient-based certainty estimates with visual attention maps.”