S
Serge Belongie
Researcher at Cornell University
Publications - 330
Citations - 123252
Serge Belongie is an academic researcher from Cornell University. The author has contributed to research in topics: Image segmentation & Object detection. The author has an hindex of 99, co-authored 329 publications receiving 90315 citations. Previous affiliations of Serge Belongie include University of California, San Diego & Google.
Papers
More filters
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Feature Pyramid Networks for Object Detection
TL;DR: This paper exploits the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost and achieves state-of-the-art single-model results on the COCO detection benchmark without bells and whistles.
Journal ArticleDOI
Shape matching and object recognition using shape contexts
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Posted Content
Feature Pyramid Networks for Object Detection
TL;DR: Feature pyramid networks (FPNets) as mentioned in this paper exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost.
The Caltech-UCSD Birds-200-2011 Dataset
TL;DR: CUB-200-2011 as mentioned in this paper is an extended version of CUB200, which roughly doubles the number of images per category and adds new part localization annotations, annotated with bounding boxes, part locations, and at-ribute labels.