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Large Scale Visual Recognition Challenge (ILSVRC) 2013:
Detection spotlights
Toronto A team
ICCV’2013
Sydney, Australia
Latent Hierarchical Model with GPU Inference for Object Detection
Yukun Zhu, Jun Zhu, Alan Yuille UCLA Computer Vision Lab
ILSVRC 2013 Spotlight
Thank L. Zhu, Y. Chen, A. Yuille and W. Freeman for the work “Latent hierarchical structural learning for object detection”in CVPR 2010.
Root-Part Configuration
Model for HorseModel for Car
Hierarchical Model
Latent Hierarchical Model with GPU Inference for Object Detection
Latent Hierarchical Model with GPU Inference for Object Detection
• The latent hierarchical model encoding holistic object and parts w.r.t. viewpoint variations
• Support richer appearance features: HOG, color, etc.
• Fast training with incremental concave-convex procedure (iCCCP) algorithm
• Quick model inference via GPU (CUDA) implementation
[1] Felzenszwalb P, McAllester D, Ramanan D, “A discriminatively trained, multiscale, deformable part model,” Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008: 1-8.
[2] Felzenszwalb P F, Girshick R B, McAllester D, “Cascade object detection with deformable part models,” Computer vision and pattern recognition (CVPR), 2010 IEEE conference on. IEEE, 2010: 2241-2248.
Latent Hierarchical Model with GPU Inference for Object Detection
ILSVRC2013 Task 1: Detection
Team name: DeltaMembers: Che-Rung Lee, Hwann-Tzong Chen, Hao-Ping Kang, Tzu-Wei Huang, Ci-Hong Deng, Hao-Che KaoNational Tsing Hua University
Generic Object Detector
ConvNet Multiclass Classifier
~ 15 proposals per image
each proposal gets one of the (200+backgrounds) class-labels
Multiclass classifier: cuda-convnet [Krizhevsky et al.] Training: 590,000 bounding boxes, 3 days using 2 GPUs0.5 error rate for classifying the validation bounding boxes
Generic object detector: “What is an object” + salient region segmentation 0.28 mAP on the validation images (ignoring class labels)
Overall: 0.057 mAP on validation data, 0.06 mAP on test data
8:30 Classification&localization
10:30 Detection
Noon Discussion panel
14:00 Invited talk by Vittorio Ferrari: Auto-annotation and self-assessment in ImageNet
14:40 Fine-Grained Challenge 2013
Agenda
http://www.image-net.org/challenges/LSVRC/2013/iccv2013
8:50 9:05 9:20 9:35 9:50 Spotlights
10:50 11:10 11:30 11:40Spotlights