A key reason for the lack of reliability of deep neural networks in the real world is their heavy reliance on spurious input features that are not essential to the true label. Focusing on image classifications, we define core attributes as the set of visual features that are always a part of the object definition while spurious attributes are the ones that are likely to co-occur with the object but not a part of it (e.g., attribute "fingers" for class "band aid"). Traditional methods for discovering spurious features either require extensive human annotations (thus, not scalable), or are useful on specific models. In this work, we introduce a general framework to discover a subset of spurious and core visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision. Our methodology is based on this key idea: to identify spurious or core visual attributes used in model predictions, we identify spurious or core neural features (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature). We then show that these neural feature annotations generalize extremely well to many more images without any human supervision. We use the activation maps for these neural features as the soft masks to highlight spurious or core visual attributes. Using this methodology, we introduce the Salient Imagenet dataset containing core and spurious masks for a large set of samples from Imagenet. Using this dataset, we show that several popular Imagenet models rely heavily on various spurious features in their predictions, indicating the standard accuracy alone is not sufficient to fully assess model' performance specially in safety-critical applications.