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5 papers · 2 filters
Stacked Generative Adversarial Networks
Xun Huang, Yixuan Li, Omid Poursaeed +2
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up…
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick +3
Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But recent deep learning object detectors have avoided pyramid representati…
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
Andreas Veit, Michael Wilber, Serge Belongie
In this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length. Moreover, residual networks see…
Can we still avoid automatic face detection?
Michael J. Wilber, Vitaly Shmatikov, Serge Belongie
After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are be…
COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images
Andreas Veit, Tomas Matera, Lukas Neumann +2
This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal…