1. Extreme, Non-parametric Object Recognition 80 million tiny images (Torralba et al)

2. Our World is Boring… Slide by Antonio Torralba

3. Lots Of Images A. Torralba, R. Fergus, W.T.Freeman. PAMI 2008

4. Lots Of Images A. Torralba, R. Fergus, W.T.Freeman. PAMI 2008

5. Lots Of Images

6. Automatic Colorization Result Grayscale input High resolution Colorization of input using average A. Torralba, R. Fergus, W.T.Freeman. 2008

7. Automatic Orientation Many images have ambiguous orientation Look at top 25% by confidence: Examples of high and low confidence images: Slide by Antonio Torralba

8. Automatic Orientation Examples A. Torralba, R. Fergus, W.T.Freeman. 2008

9. What If we have Labels…

11. Are 32x32 images enough?

12. 10% of the objects account for 90% of the data ~Zipf’s law Caltech 101 Tiny images LabelMe Slide by Antonio Torralba

13. Do people do this?

14. What’s the Capacity of Visual Long Term Memory? “Basically, my recollection is that we just separated the pictures into distinct thematic categories: e.g. cars, animals, single- person, 2-people, plants, etc.) Only a few slides were selected which fell into each category, and they were visually distinct.” According to Standing Standing (1973) 10,000 images 83% Recognition What we know… What we don’t know… Sparse Details Dogs Playing Cards “Gist” OnlyHighly Detailed … people can remember thousands of images … what people are remembering for each item? High Fidelity Visual Memory is possible (Hollingworth 2004) Slide by Aude Oliva

15. Massive Memory I: Methods... Showed 14 observers 2500 categorically unique objects 1 at a time, 3 seconds each 800 ms blank between items Study session lasted about 5.5 hours Repeat Detection task to maintain focus 1-back Followed by 300 2-alternative forced choice tests 1024-back Slide by Aude Oliva

17. how far can we push the fidelity of visual LTM representation ? Same object, different states Slide by Aude Oliva

18. Visual Cognition Expert Predictions 92% Massive Memory I: Recognition Memory Results Replication of Standing (1973) Slide by Aude Oliva

19. 92%88%87% Massive Memory I: Recognition Memory Results Slide by Aude Oliva

20. Extrapolation of Repeat Detection Data Human performances for n = 1024 Power law (r 2 =.988) Quadratic (r 2 =.988) Brady, Konkle, Alvarez, Oliva (submitted) Slide by Aude Oliva

21. Past and future of image datasets in computer vision Lena a dataset in one picture 1972 10 0 10 5 10 10 20 Number of pictures 10 15 Human Click Limit (all humanity taking one picture/second during 100 years) Time 1996 40.000 COREL 2007 2 billion 2020? Slide by Antonio Torralba