  We present a set of metrics that utilize vision priors to effectively assess
the performance of saliency methods on image classification tasks. To
understand behavior in deep learning models, many methods provide visual
saliency maps emphasizing image regions that most contribute to a model
prediction. However, there is limited work on analyzing the reliability of
saliency methods in explaining model decisions. We propose the metric
COnsistency-SEnsitivity (COSE) that quantifies the equivariant and invariant
properties of visual model explanations using simple data augmentations.
Through our metrics, we show that although saliency methods are thought to be
architecture-independent, most methods could better explain transformer-based
models over convolutional-based models. In addition, GradCAM was found to
outperform other methods in terms of COSE but was shown to have limitations
such as lack of variability for fine-grained datasets. The duality between
consistency and sensitivity allow the analysis of saliency methods from
different angles. Ultimately, we find that it is important to balance these two
metrics for a saliency map to faithfully show model behavior.
