conference-quality

1、16-721: Learning-based Methods in VisionStaff:Instructor: Alexei (Alyosha) Efros (efroscs), 4207 NSHTA: Jean-Francois Lalonde (jlalondecs), A521 NSHWeb Page:/efros/courses/LBMV07/TodayIntroductionWhy This Course?Administrative stuffOverview of the courseImage DatasetsProjects / ChallengesA bit about

2、 meAlexei (Alyosha) EfrosRelatively new faculty (RI/CSD)Ph.D 2003, from UC Berkeley (signed by Arnie!)Research Fellow, University of Oxford, 03-04TeachingI am still learningThe plan is to have fun and learn cool things, both you and me!Social warning: I dont see well ResearchVision, Graphics, Data-d

3、riven “stuff”P(pán)hD Thesis on Texture and Action SynthesisAntonio Criminisis son cannot walk but he can flySmart Erase button in Microsoft Digital Image Pro:Why this class?The Old Days:1. Graduate Computer Vision2. Advanced Machine PerceptionWhy this class?The New and Improved Days:1. Graduate Computer

4、 Vision2. Advanced Machine PerceptionPhysics-based Methods in VisionGeometry-based Methods in VisionLearning-based Methods in VisionDescribing Visual Scenes using Transformed Dirichlet Processes. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. NIPS, Dec. 2005. The Hip & Trendy LearningLearning

5、 as Last ResortLearning as Last Resortfrom Sinha and Adelson 1993EXAMPLE: Recovering 3D geometry from single 2D projection Infinite number of possible solutions! Learning-based Methods in VisionThis class is about trying to solve problems that do not have a solution! Dont tell your mathematician fri

6、neds!This will be done using Data:E.g. what happened before is likely to happen againGoogle Intelligence (GI): The AI for the post-modern world!Why is this even useful?Even a decade ago at ICCV99 Faugeras claimed it wasnt!The Vision Story Begins“What does it mean, to see? The plain mans answer (and

7、Aristotles, too). would be, to know what is where by looking.” - David Marr, Vision (1982) Vision: a split personality“What does it mean, to see? The plain mans answer (and Aristotles, too). would be, to know what is where by looking. In other words, vision is the process of discovering from images

8、what is present in the world, and where it is.” Answer #1: pixel of brightness 243 at position (124,54) and depth .7 metersAnswer #2: looks like bottom edge of whiteboard showing at the top of the imageWhich Do we want?Is the difference just a matter of scale? depth mapMeasurement vs. PerceptionBrig

9、htness: Measurement vs. PerceptionBrightness: Measurement vs. PerceptionProof!Lengths: Measurement vs. Perceptionhttp:/www.michaelbach.de/ot/sze_muelue/index.html Mller-Lyer IllusionVision as Measurement DeviceReal-time stereo on MarsStructure from MotionPhysics-based VisionVirtualized Realitybut wh

10、y do Learning for Vision?“What if I dont care about this wishy-washy human perception stuff? I just want to make my robot go!” Small Reason: For measurement, other sensors are often better (in DARPA Grand Challenge, vision was barely used!)For navigation, you still need to learn!Big Reason: The goal

11、s of computer vision (what + where) are in terms of what humans care about.So what do humans care about?slide by Fei Fei, Fergus & Torralba Verification: is that a bus?slide by Fei Fei, Fergus & Torralba Detection: are there cars?slide by Fei Fei, Fergus & Torralba Identification: is that a picture

12、of Mao?slide by Fei Fei, Fergus & Torralba Object categorizationskybuildingflagwallbannerbuscarsbusfacestreet lampslide by Fei Fei, Fergus & Torralba Scene and context categorization outdoor city traffic slide by Fei Fei, Fergus & Torralba Rough 3D layout, depth orderingChallenges 1: view point vari

13、ationMichelangelo 1475-1564Challenges 2: illuminationslide credit: S. UllmanChallenges 3: occlusionMagritte, 1957 Challenges 4: scaleslide by Fei Fei, Fergus & Torralba Challenges 5: deformationXu, Beihong 1943Challenges 6: background clutterKlimt, 1913Challenges 7: object intra-class variationslide

14、 by Fei-Fei, Fergus & Torralba Challenges 8: local ambiguityslide by Fei-Fei, Fergus & Torralba Challenges 9: the world behind the image In this course, we will:Take a few baby stepsGoalsRead some interesting papers togetherLearn something new: both you and me!Get up to speed on big chunk of vision

15、researchunderstand 70% of CVPR papers!Use learninig-based vision in your own workTry your hand in a large vision projectLearn how to speakLearn how think critically about papersCourse OrganizationRequirements:Paper Presentations (50%)Paper PresenterPaper EvaluatorClass Participation (20%)Keep annota

16、ted bibliography Ask questions / debate / flight / be involved!Final Project (30%)Do something with lots of data (at least 500 images)Groups of 1 or 2Paper AdvocatePick a paper from list That you like and willing to defendSometimes I will make you do two papers, or backgroundMeet with me before star

17、ting, to talk about how to present the paper(s)Prepare a good, conference-quality presentation (20-45 min, depending on difficulty of material) Meet with me again 2 days before class to go over the presentationOffice hours at end of each classPresent and defend the paper in front of classPaper Evalu

18、ator For some papers, we will have EvaluatorsSign up for a paper you find interestingGet the code online (or implement if easy)Run it on a toy problem, play with parametersRun it on a new datasetPrepare short 10-15 min presentation detailing resultsDiscuss the paper criticallyClass ParticipationKeep

19、 annotated bibliography of papers you read (always a good idea!). The format is up to you. At least, it needs to have:Summary of key pointsA few Interesting insights, “aha moments”, keen observations, etc.Weaknesses of approach. Unanswered questions. Areas of further investigation, improvement.Submit your thoughts for current paper