End-to-end training of object class detectors for mean average precision

Introduction to Modern Information Retrieval

Salton, G., McGill, M.J.: · 1986

Earlier work this paper cites.

IR evaluation methods for retrieving highly relevant documents

Järvelin, K., Kekäläinen, J.: · 2000

Earlier work this paper cites.

Rapid object detection using a boosted cascade of simple features

Viola, P., Jones, M.: · 2001

Earlier work this paper cites.

Histogram of Oriented Gradients for human detection

Dalal, N., Triggs, B.: · 2005

Earlier work this paper cites.

A support vector method for optimizing average precision

Yue, Y., Finley, T., Radlinski, F., Joachims, T.: · 2007

Earlier work this paper cites.

Learning to localize objects with structured output regression

Blaschko, M.B., Lampert, C.H.: · 2008

Earlier work this paper cites.

SoftRank: Optimising non-smooth rank metrics

Taylor, M., Guiver, J., Robertson, S., Minka, T.: · 2008

Earlier work this paper cites.

Combining efficient object localization and image classification

Harzallah, H., Jurie, F., Schmid, C.: · 2009

Earlier work this paper cites.

What is an object?

Alexe, B., Deselaers, T., Ferrari, V.: · 2010

Earlier work this paper cites.

The PASCAL Visual Object Classes (VOC) Challenge

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2010

Earlier work this paper cites.

Object detection with discriminatively trained part based models

Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: · 2010

Cited alongside, same era.

Imagenet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012

Cited alongside, same era.

Selective search for object recognition

Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: · 2013

Cited alongside, same era.

Caffe: An open source convolutional architecture for fast feature embedding

Jia, Y.: · 2013

Cited alongside, same era.

Overfeat: Integrated recognition, localization and detection using convolutional networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2014

Cited alongside, same era.

Rich feature hierarchies for accurate object detection and semantic segmentation

Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014

Very deep convolutional networks for large-scale image recognition

Simonyan, K., Zisserman, A.: · 2015

Later among the works it cites.

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., Darrell, T.: · 2015

Later among the works it cites.

Show and tell: A neural image caption generator

Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: · 2015

Later among the works it cites.

Flowing convnets for human pose estimation in videos

Pfister, T., Charles, J., Zisserman, A.: · 2015

Later among the works it cites.

The PASCAL visual object classes challenge: A retrospective

Everingham, M., Eslami, S., van Gool, L., Williams, C., Winn, J., Zisserman, A.: · 2015

Later among the works it cites.

ImageNet large scale visual recognition challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., Fei-Fei, L.: · 2015