Perspective: Leveraging Human Understanding for Identifying and Characterizing Image Atypicality

High-quality data plays a vital role in developing reliable image classification models. Despite that, what makes an image difficult to classify remains an unstudied topic. This paper provides a first-of-its-kind, model-agnostic characterization of image atypicality based on human understanding. We consider the setting of image classification...

What Should You Know? A Human-In-the-Loop Approach to Unknown Unknowns Characterization in Image Recognition

Unknown unknowns represent a major challenge in reliable image recognition. Existing methods mainly focus on unknown unknowns identification, leveraging human intelligence to gather images that are potentially difficult for the machine. To drive a deeper understanding of unknown unknowns and more effective identification and treatment, this...

Detecting, classifying, and mapping retail storefronts using street-level imagery

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep...