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 Poster ID: 2-46

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

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

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

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

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

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

8. Results: improve multiple detectors! Can employ any object 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! International Conference on Computer Vision 2011 Poster ID: 2-46 Visit our project page: http://chenlab.ece.cornell.edu/projects/AdaptiveContext/