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