Segmentation Results: VOC2010 BETA

Competition "comp6" (train on own data)

This leaderboard shows only those submissions that have been marked as public, and so the displayed rankings should not be considered as definitive.

Average Precision (AP %)

  mean

aero
plane
bicycle

bird

boat

bottle

bus

car

cat

chair

cow

dining
table
dog

horse

motor
bike
person

potted
plant
sheep

sofa

train

tv/
monitor
submission
date
Oxford_TVG_CRF_RNN_COCO [?] 75.792.258.587.566.870.692.185.384.736.480.465.680.983.986.376.864.081.251.481.869.722-Apr-2015
Oxford_TVG_CRF_RNN_VOC [?] 73.688.140.190.166.669.592.281.883.834.382.558.483.581.882.477.267.081.743.780.666.722-Apr-2015
TTI_zoomout [?] 64.482.435.977.355.858.082.976.876.622.370.652.472.877.275.764.948.169.036.470.656.425-Nov-2014
Berkeley-poselets-align-pb [?] -49.723.320.619.047.158.153.632.5-31.1-29.542.941.943.816.639.018.438.041.530-Aug-2010

Abbreviations

TitleMethodAffiliationContributorsDescriptionDate
Segmentation on poselet detectionsBerkeley-poselets-align-pbUC BerkeleyThomas Brox, Lubomir Bourdev, Subhransu Maji, Jitendra Malik Non-rigid alignment of poselet activations to UCM edges, filling of area between object edges by variational smoothing, competition among objects, and competitive refinement of masks2010-08-30 21:00:35
Oxford_TVG_CRF_RNN_COCOOxford_TVG_CRF_RNN_COCO[1] University of Oxford / [2] Baidu IDLShuai Zheng [1]; Sadeep Jayasumana [1]; Bernardino Romera-Paredes [1]; Chang Huang [2]; Philip Torr [1]We introduce a new form of convolutional neural network, called CRF-RNN, which expresses Dense Conditional Random Fields (Dense CRF) as a Recurrent Neural Network (RNN). We plug this CRF-RNN network into an existing deep CNN to obtain a system that combines the desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF inference with CNNs, making it possible to train the whole system end-to-end with the usual back-propagation algorithm. The system used for this submission was trained on VOC 2012 Segmentation challenge train data, Berkeley augmented data and a subset of COCO 2014 train data. More details will be available in the paper http://arxiv.org/abs/1502.03240.2015-04-22 14:22:55
Oxford_TVG_CRF_RNN_VOCOxford_TVG_CRF_RNN_VOC[1] University of Oxford / [2] Baidu IDLShuai Zheng [1]; Sadeep Jayasumana [1]; Bernardino Romera-Paredes [1]; Chang Huang [2]; Philip Torr [1]We introduce a new form of convolutional neural network, called CRF-RNN, which expresses Dense Conditional Random Fields (Dense CRF) as a Recurrent Neural Network (RNN). We plug this CRF-RNN network into an existing deep CNN to obtain a system that combines the desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF with CNNs, making it possible to train the whole system end-to-end with the usual back-propagation algorithm. The system used for this submission was trained on VOC 2012 Segmentation challenge train data, and Berkeley augmented data (COCO dataset was not used). More details will be available in the paper http://arxiv.org/abs/1502.03240. 2015-04-22 11:12:44
Feedforward segmentation with zoom-out featuresTTI_zoomoutTTI-ChicagoMohammadreza Mostajabi, Payman Yadollahpour, Gregory ShakhnarovichSame as before, except using VGG 16-layer network instead of VGG CNN-S network. Fine-tuning on VOC-2012 was not performed. See http://arxiv.org/abs/1412.0774 for details.2014-11-25 18:42:29