Locality in space and time for data-efficient visual recognition

Spatial localization in time is vital for humans. Therefore we desire that computer vision algorithms are also able to spatially and temporally localize objects and actions. These algorithms generally learn from given data and discover patterns, parts, motions, and their locations by exploiting inductive biases that are essential for learning....

Humans Disagree With the IoU for Measuring Object Detector Localization Error

The localization quality of automatic object detectors is typically evaluated by the Intersection over Union (IoU) score. In this work, we show that humans have a different view on localization quality. To evaluate this, we conduct a survey with more than 70 participants. Results show that for localization errors with the exact same IoU score,...