Deep neural networks (DNNs) have achieved state-of-the-art results in a broad range of
tasks, in particular the ones dealing with the perceptual data. However, full-scale application …

Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …

Over the last decade, Convolutional Neural Network (CNN) models have been highly
successful in solving complex vision based problems. However, deep models are perceived …

Weakly supervised learning with only coarse labels can obtain visual explanations of deep
neural network such as attention maps by back-propagating gradients. These attention …

We propose D-RISE, a method for generating visual explanations for the predictions of
object detectors. Utilizing the proposed similarity metric that accounts for both localization …

The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the
entire flow of visual processing from input to resulting fixations. Despite many replications …

We propose DeepGRU, a novel end-to-end deep network model informed by recent
developments in deep learning for gesture and action recognition, that is streamlined and …

Thanks to the rapid advances in the deep learning techniques and the wide availability of
large-scale training sets, the performances of video saliency detection models have been …

Video-based person re-identification (Re-ID) aims at retrieving the person through the video
sequences across non-overlapping cameras. Some characteristics of pedestrians are not …

Conventionally, AI models are thought to trade off explainability for lower accuracy. We
develop a training strategy that not only leads to a more explainable AI system for object …