Presentation on theme: "Learning Shared Body Plans Ian Endres University of Illinois work with Derek Hoiem, Vivek Srikumar and Ming-Wei Chang."— Presentation transcript:
Learning Shared Body Plans Ian Endres University of Illinois work with Derek Hoiem, Vivek Srikumar and Ming-Wei Chang
How should we represent multiple related object categories?
Want to detect, localize, and estimate pose of broad range of objects, including new ones
One option: independent detectors Cat Detector Dog Detector 4-Legged Animal Detector Basic-Level Categories Broad Categories Parts … Head Detector
Our previous work: Train separate detectors, Joint spatial model Vehicle Wheel Animal Leg Head Four-legged Mammal Can run Can Jump Facing right Moves on road Facing right Farhadi Endres Hoiem (2010)
Jointly trained multi-category models Train part/category detectors to jointly predict object structure – Only need to perform well in context defined by others Spatial model encodes likely part positions, number of parts, likely categories, etc. – Generalizes Felzenszwalb et al.: cross-category sharing, multiple parts with one model, variable size
Detection with Deformable Part Models From Felzenszwalb et al.
Shared mixture of deformable parts: Body Plans Include a body plan for background patches: No appearance models, just a bias
Body Plan Overview Object Center + + + Head Anchors High Scoring Detections
Anchor Point Score S a = bias + appearance score - deformation cost HOG based Deformable part model (Felzenszwalb et al.) Quadratic penalty in position and scale S a = bias + appearance score - deformation cost Overall score must be greater than 0 to be detected