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Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights
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Toronto A team
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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.
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Root-Part Configuration Model for Horse Model for Car Hierarchical Model Latent Hierarchical Model with GPU Inference for Object Detection
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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
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[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
![[1] Felzenszwalb P, McAllester D, Ramanan D, A discriminatively trained, multiscale, deformable part model, Computer Vision and Pattern Recognition, 2008.](http://images.slideplayer.com/14/4485659/slides/slide_7.jpg "CVPR 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: Latent Hierarchical Model with GPU Inference for Object Detection.")
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ILSVRC2013 Task 1: Detection Team name: Delta Members: Che-Rung Lee, Hwann-Tzong Chen, Hao-Ping Kang, Tzu-Wei Huang, Ci-Hong Deng, Hao-Che Kao National Tsing Hua University
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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 GPUs 0.5 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
![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 GPUs 0.5 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: mAP on validation data, 0.06 mAP on test data](http://images.slideplayer.com/14/4485659/slides/slide_9.jpg "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 GPUs 0.5 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: mAP on validation data, 0.06 mAP on test data")
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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 11:40 Spotlights
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