Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline

Methods for object detection and segmentation often require abundant instance-level annotations for training, which are time-consuming and expensive to collect. To address this, the task of zero-shot object detection (or segmentation) aims at learning effective methods for identifying and localizing object instances for the categories that have no supervision available. Constructing architectures for these tasks requires choosing from a myriad of design options, ranging from the form of the class encoding used to transfer information from seen to unseen categories, to the nature of the function being optimized for learning. In this work, we extensively study these design choices, and carefully construct a simple yet extremely effective zero-shot recognition method. Through extensive experiments on the MSCOCO dataset on object detection and segmentation, we highlight that our proposed method outperforms existing, considerably more complex, architectures. Our findings and method, which we propose as a competitive future baseline, point towards the need to revisit some of the recent design trends in zero-shot detection / segmentation.Comment: 17 Pages, 7 Figure

Utility of Digital Stereo Images for Optic Disc Evaluation

The utility of digital stereo optic disc images for glaucoma was assessed by comparing primary digital stereo images to 35-mm slides and scanned and grayscale images. Whereas color seemingly adds little to stereo grayscale images, digital images are useful for evaluating the optic disc in stereo and should allow the application of advanced image processing techniques