Ross B. Girshick

Object Detection with Discriminatively Trained Part Based Models

Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, Deva Ramanan
IEEE Transactions on Pattern Analysis and Machine…
2009

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in… (More)

Caffe: Convolutional Architecture for Fast Feature Embedding

Yangqing Jia, Evan Shelhamer, +5 authors Trevor Darrell
ACM Multimedia
2014

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a… (More)

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross B. Girshick, Jian Bo Sun
IEEE Transactions on Pattern Analysis and Machine…
2015

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet <xref ref-type="bibr" rid="ref1">[1]</xref> and Fast R-CNN <xref… (More)

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Ross B. Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik
2014 IEEE Conference on Computer Vision and…
2014

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine… (More)

Fast R-CNN

Ross B. Girshick
2015 IEEE International Conference on Computer…
2015

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep… (More)

You Only Look Once: Unified, Real-Time Object Detection

Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, Ali Farhadi
2016 IEEE Conference on Computer Vision and…
2016

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to… (More)

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Saurabh Gupta, Ross B. Girshick, Pablo Andrés Arbeláez, Jitendra Malik
ECCV
2014

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height… (More)

Cascade object detection with deformable part models

Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester
2010 IEEE Computer Society Conference on Computer…
2010

We describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. We focus primarily on the case of star-structured models and show how a… (More)

Part-Based R-CNNs for Fine-Grained Category Detection

Ning Zhang, Jeff Donahue, Ross B. Girshick, Trevor Darrell
ECCV
2014

Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-normalized… (More)

Mask R-CNN

Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross B. Girshick
2017 IEEE International Conference on Computer…
2017

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality… (More)

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