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Found 5 papers, 1 papers with code
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models
Robots need to have a memory of previously observed, but currently occluded objects to work reliably in realistic environments.
Towards Trainable Saliency Maps in Medical Imaging
While success of Deep Learning (DL) in automated diagnosis can be transformative to the medicinal practice especially for people with little or no access to doctors, its widespread acceptability is severely limited by inherent black-box decision making and unsafe failure modes.
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
Vision-based robotics often separates the control loop into one module for perception and a separate module for control.
Federated Learning for Breast Density Classification: A Real-World Implementation
Building robust deep learning-based models requires large quantities of diverse training data.
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Saliency maps have become a widely used method to make deep learning models more interpretable by providing post-hoc explanations of classifiers through identification of the most pertinent areas of the input medical image.
