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Extracting Adaptive Contextual Cues From Unlabeled Regions

Extracting Adaptive Contextual Cues From Unlabeled Regions. Congcong Li + , Devi Parikh * , Tsuhan Chen + + Cornell University * Toyota Technological Institute at Chicago. Object Detection with Context. Previous: Focus on labeled objects but neglect unlabeled regions . plant. plant.

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Extracting Adaptive Contextual Cues From Unlabeled Regions

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  1. Extracting Adaptive Contextual Cues From Unlabeled Regions Congcong Li+, Devi Parikh*, Tsuhan Chen+ + Cornell University * Toyota Technological Institute at Chicago International Conference on Computer Vision 2011

  2. Object Detection with Context Previous: Focus on labeled objects but neglect unlabeled regions plant plant chair sofa

  3. Is unlabeled region useful? Labeled vsUnlabeled 45% 55% PASCAL 07 dataset Human Study: unlabeled regions help 28% 72% MSRC dataset

  4. Our View: Leverage unlabeled regions ‘plant’ context plant

  5. Our view: Extract adaptive context • Prior works: Context at fixed granularity Scene: whole image EXO: expand fixed ratio • Ours: Context at adaptive granularities Multi-level Interactions! Intra-object Inter-object Scene Contextual-Meta Objects (CMO) 20%

  6. Algorithm: discovering contextual regions Database ... … Extent-based Clustering Content-based Clustering Learn “object” Models ... … Context Detector

  7. Results on PASCAL 2007 Unlabeled: complementary context Adaptive granularity helps! adaptive fixed granularity

  8. Results: improve multiple detectors! Can employ anyobject detector to learn the contextual “object”！

  9. Results: provide spatial prior for OOI

  10. Contributions • Extracting contextual cues from unlabeled regions • Capturing contextual interactions at varying levels: Scene, Inter-object, Intra-object • Extracting contextual regions by learning “object” models using any object detector • Intelligently leveraging existing techniques: easily accessible to community

  11. Thank you! Visit our project page: http://chenlab.ece.cornell.edu/projects/AdaptiveContext/ International Conference on Computer Vision 2011

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