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Fcv bio cv_cottrell
- 1. Unsupervised learning of visual representations and their use in object & face recognition Gary Cottrell Chris Kanan Honghao Shan Lingyun Zhang Matthew Tong Tim Marks
- 2. Collaborators Honghao Shan Chris Kanan
- 3. Collaborators Tim Marks Matt Tong Lingyun Zhang
- 5. Efficient Encoding of the world leads to magno- and parvo-cellular response properties… This suggests that these cell types exist because they are useful for efficiently encoding the temporal dynamics of the world. Trained on grayscale images Trained on color images Spatial extent Temporal extent Trained on video cubes Midget? Parasol? Persistent, small Transient, large
- 19. Stored memories of Bob Stored memories of Alice New fragments Result: 7 votes for Alice, only 3 for Bob. It’s Alice!
- 24. Belief After 1 Fixation Belief After 10 Fixations
- 26. Cal Tech 101: 101 Different Categories AR dataset: 120 Different People with different lighting, expression, and accessories
- 34. Again, best for single feature-type systems and for 1 training instance better than all systems
- 38. Thanks!
- 40. The SPCA model as a neural net… It is A T that is mostly 0 …
Notas del editor
- 12/30/11 8:40 -
- 12/30/11 This is in stark contrast to the predominant methods used in computer vision, and even many models in computational neurosciece Line 1: one-shot system Line 2: active vision Note that the bottom approach is primate-like (although pretty dumbed down) Note that I’m leaving out most of the details
- 12/30/11 Humans make ~170,000 saccades each day
- 12/30/11 Explain how it uses a saliency map to acquire information and how as it serially acquires more information over time NIMBLE becomes more confident about the correct category.
- 12/30/11 ~64 fixations required to achieve 99% of maximum accuracy Averaged over 10 cross validation runs
- 12/30/11 Note that this is a comparison versus the best results using a single feature type and looks at percent improvement in performance (not absolute improvement, so it is 1 - (Nimble Perf / Best One-Desc Perf) Mention training instances on X-axis
- 12/30/11 Note again that NIMBLE performs very well using few training images even when dealing with disguises
- 12/30/11 We showed that NIMBLE is not a toy cognitive model, but one with real-world applicability This work was supported by the NSF (grant #SBE-0542013) to the Temporal Dynamics of Learning Center., G.W. Cottrell, PI.
- Include in the overview information on the purpose and mission of the SLC, the strategic concept and milestones, achievements, new directions; the organization of the research thrusts (or equivalent); value of the Center mode; and the integrative nature and relationship of all following presentations (scientific and other) to research and overall vision of Center. The Center’s vision should address each of the SLC program goals: advancing the frontiers of the science of learning through integrated research; connecting this research to specific scientific, technological, educational, and workforce challenges; and enabling research communities that can capitalize on new opportunities and discoveries and respond to new challenges.
- Include in the overview information on the purpose and mission of the SLC, the strategic concept and milestones, achievements, new directions; the organization of the research thrusts (or equivalent); value of the Center mode; and the integrative nature and relationship of all following presentations (scientific and other) to research and overall vision of Center. The Center’s vision should address each of the SLC program goals: advancing the frontiers of the science of learning through integrated research; connecting this research to specific scientific, technological, educational, and workforce challenges; and enabling research communities that can capitalize on new opportunities and discoveries and respond to new challenges.