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Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights

Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights. Toronto A team. ILSVRC 2013 Spotlight. Latent Hierarchical Model with GPU Inference for Object Detection Yukun Zhu, Jun Zhu, Alan Yuille UCLA Computer Vision Lab.

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Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights

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  1. Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights

  2. Toronto A team

  3. ILSVRC 2013 Spotlight Latent Hierarchical Model with GPU Inference for Object Detection Yukun Zhu, Jun Zhu, Alan Yuille UCLA Computer Vision Lab Thank L. Zhu, Y. Chen, A. Yuille and W. Freeman for thework “Latent hierarchical structural learning for object detection”in CVPR 2010.

  4. Latent Hierarchical Model with GPU Inference for Object Detection Root-Part Configuration Hierarchical Model Model for Horse Model for Car

  5. 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

  6. 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. 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.

  7. ILSVRC2013 Task 1: DetectionTeam name: DeltaMembers: Che-Rung Lee, Hwann-Tzong Chen, Hao-Ping Kang, Tzu-Wei Huang, Ci-Hong Deng, Hao-CheKaoNational Tsing Hua University

  8. Generic Object Detector ~ 15 proposals per image ConvNet Multiclass Classifier each proposal gets one of the (200+backgrounds) class-labels • Generic object detector: “What is an object” + salient region segmentation • 0.28 mAP on the validation images (ignoring 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 Overall: 0.057 mAPon validation data, 0.06 mAPon test data

  9. Agenda 8:30Classification&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 Spotlights 9:50 9:35 8:50 9:20 9:05 Spotlights 11:10 11:40 11:30 10:50 http://www.image-net.org/challenges/LSVRC/2013/iccv2013

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