Best of both worlds: human-machine collaboration for object annotation Olga Russakovsky1 Li-Jia Li2 Li Fei-Fei1 1 Stanford University 2 Snapchat∗ Abstract

First Page		Document Content
Date: 2015-04-12 19:52:49 Machine learning Image segmentation ImageNet Conference on Computer Vision and Pattern Recognition Automatic image annotation Minimum bounding box Pattern recognition Object detection Computer vision		Best of both worlds: human-machine collaboration for object annotation Olga Russakovsky1 Li-Jia Li2 Li Fei-Fei1 1 Stanford University 2 Snapchat∗ Abstract Add to Reading List Source URL: ai.stanford.edu Download Document from Source Website File Size: 3,90 MB Share Document on Facebook

	Hierarchical segmentation of graphical interfaces for Document Object Model reconstruction C˘at˘alin F. Pert, icas, 1 Mihai S. Baba 1 Homa Davoudi 1 R˘azvan V. Florian 1 Figure 1. Detection of graphical elements usin DocID: 1xVN5 - View Document
	Feature Pyramid Networks for Object Detection Tsung-Yi Lin1,2 , Piotr Doll´ar1 , Ross Girshick1 , Kaiming He1 , Bharath Hariharan1 , and Serge Belongie2 1 arXiv:1612.03144v2 [cs.CV] 19 Apr 2017 DocID: 1xU2y - View Document
	Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition Tim Althoff, Hyun Oh Song, Trevor Darrell UC Berkeley EECS/ICSI Multimedia Event Detection DocID: 1xTRG - View Document
	NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles Jiajun Lu∗, Hussein Sibai∗, Evan Fabry, David Forsyth University of Illinois at Urbana Champaign {jlu23, sibai2, efabry2, daf}@il DocID: 1xTHf - View Document
	Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition Tim Althoff UC Berkeley EECS/ICSI DocID: 1xTvu - View Document