Dynamic Recognition on a Budget

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Ζεβεδαῖος Αναστασιάδης
7 χρόνια πριν
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1 Dynamic Recognition on a Budget Sergey Karayev Mario Fritz Trevor Darrell

2 Logo courtesy of Jon Barron. I work in computer vision.

3 With a test time budget, cannot compute all features. SIFT HOG LBP SSIM color Textons

4 With different instances benefiting from different features, selection needs to be dynamic.

5 f R or

6 Feature selection f 0 0 β 3 β 4 Insufficient when budget is Anytime, or when budgeted across multiple instances.

7 Cascade Viola & Jones (CVPR 2001) R Two actions: Reject and Continue.

8 Minimizing feature cost Chen et al. (ICML 2012), and others. R Optimize both stage thresholds and order by considering feature computation cost. L(f) =l(f)+ρr(f) }{{} regularized risk + λc(f) }{{} test cost

10 Dynamic Feature Selection Branch on selected feature values. Be able to classify from any state.

11 Active Classification Gao & Koller (NIPS 2011)

12 Active Classification Gao & Koller (NIPS 2011) Action selection: greedy, based on expected information gain in mixture of Gaussians model (predicted by nearest neighbors in dataset).

13 Active Classification Gao & Koller (NIPS 2011) Action selection: greedy, based on expected information gain in mixture of Gaussians model (predicted by nearest neighbors in dataset). Feature combination: same mixture of Gaussians model.

14 Timely Object Recognition Karayev et al. (NIPS 2012)

15 Timely Object Recognition Karayev et al. (NIPS 2012) Action selection: non-myopic linear policy learned by MDP, with manually defined reward.

16 Timely Object Recognition Karayev et al. (NIPS 2012) Action selection: non-myopic linear policy learned by MDP, with manually defined reward. Feature combination: inference in a graphical model (but isn t the point).

17 Dynamic Feature Selection for Classification Karayev et al. (current)

18 Dynamic Feature Selection for Classification Karayev et al. (current) Action selection: non-myopic policy learned by MDP.

19 Dynamic Feature Selection for Classification Karayev et al. (current) Action selection: non-myopic policy learned by MDP. Feature combination: linear.

20 General agent-based approach. Classification Classification update state with observations receive reward (in training) classification minimize loss State State State action selection maximize expected value Observations Observations etc Action Action Action Time execute action receive feature value

21 Classification Classification update state with observations receive reward (in training) classification minimize loss State State State action selection maximize expected value Observations Observations etc Action Action Action MDP Time execute action receive feature value

22 Definition 2. The feature selection MDP consists of the tuple (S, A,T( ),R( ),γ): State s Sstores the selected feature subset H π(x) and their values and total cost C Hπ(x). The set of actions A is exactly the set of features H. The (stochastic) state transition distribution T (s s, a) can depend on the instance x. The reward function R(s, a, s ) R is manually specified, and depends on the classifier g and the instance x. The discount γ determines amount of lookahead in selecting actions: if 0, actions are selected greedily based on their immediate reward; if 1, the reward accrued by subsequent actions is given just as much weight as the reward of the current action. V π (s 0 )=E ξ {π,x} r(ξ) =E ξ {π,x} [ I i=0 γ i r i ]

23 action selection maximize expected value policy: action-value function: reward definition assume linearity: learning the policy

24 action selection maximize expected value policy: action-value function: reward definition assume linearity: learning the policy

25 reward definition: Anytime performance

26 reward definition: Anytime performance

27 reward definition: Anytime performance

28 In practice, use infogain. a = h f I Hs (Y ; h f ) I Hs (Y ; h f )(B s 1 2 c f ) c f Bs I(Y ; H π(x) )=H(Y ) H(Y H π(x) )= y Y P (y)logp (y) y,h π(x) P (y, H π(x) )logp (y H π(x) )

29 action selection maximize expected value policy: action-value function: reward definition assume linearity: learning the policy

30 learning the policy Sample the expectation: collect (state, action, reward, state) tuples by executing current policy. Update the policy: solve for weights. Iterate.

31 Want to be fast Classification Classification + cost, entropy, other features update state with observations receive reward (in training) classification minimize loss State State State action selection maximize expected value Observations Observations etc Action Action Action Time execute action receive feature value

32 How do we combine arbitrary subset of features? Accuracy Subset? Cost MRF model Naive Bayes Karayev et al. (NIPS 2012) Linear model?

33 Two ideas: Missing value imputation; Learning multiple linear models.

34 Missing value imputation Mean SVD The rank-r truncated SVD of N F matrix X c can be written as ˆX c = U R D R V T R (1) in x. The unobserved values x u are filled in by V u R ( ) 1 VR ot V R o V ot R xo. Gaussian p y y x = [ ] x o x u x u x o N N ( 0, [ ]) A C C T B ( ) C T A 1 x o, B C T A 1 C (3) (4) knn

35 Missing value imputation Mean SVD The rank-r truncated SVD of N F matrix X c can be written as ˆX c = U R D R V T R (1) in x. The unobserved values x u are filled in by V u R ( ) 1 VR ot V R o V ot R xo. Gaussian p y y x = [ ] x o x u x u x o N N ( 0, [ ]) A C C T B ( ) C T A 1 x o, B C T A 1 C (3) (4) knn

36 Missing value imputation Mean SVD The rank-r truncated SVD of N F matrix X c can be written as ˆX c = U R D R V T R (1) in x. The unobserved values x u are filled in by V u R ( ) 1 VR ot V R o V ot R xo. Gaussian p y y x = [ ] x o x u x u x o N N ( 0, [ ]) A C C T B ( ) C T A 1 x o, B C T A 1 C (3) (4) knn

37 Missing value imputation Mean SVD The rank-r truncated SVD of N F matrix X c can be written as ˆX c = U R D R V T R (1) in x. The unobserved values x u are filled in by V u R ( ) 1 VR ot V R o V ot R xo. Gaussian p y y x = [ ] x o x u x u x o N N ( 0, [ ]) A C C T B ( ) C T A 1 x o, B C T A 1 C (3) (4) knn

38 Digits Scenes

39 Training on feature vectors with correctly missing value distribution. Input: D = {x n,y n } N n=1; L B Result: Trained π, g π 0 random; for i 1 to max iterations do States, Actions, Costs, Labels GatherSamples(D, π i 1 ); g i UpdateClassifier(States, Labels); Rewards ComputeRewards(States, Costs, Labels, g i, L B,γ); π i UpdatePolicy(States, Actions, Rewards); end

40 Learning multiple classifiers. In this case, 2 separate models: and

41 Digits Scenes

42 Dynamic results.

43 We evaluate the following baselines: Static, greedy: corresponds to best performance of a policy that does not observe feature values and selects actions greedily (γ =0). Static, non-myopic: policy that does not observe values but considers future action rewards (γ =1). Dynamic, greedy: policy that observes feature values, but selects actions greedily. Our method is the Dynamic, non-myopic policy: feature values are observed, with full lookahead.

44 Synthetic random optimal Example static, non-myopic dynamic, non-myopic Feature Number Cost d i : sign of dimension i D 1 q o : label of datapoint, 2 if in quadrant o D 10

45 Scenes-15 bedroom suburban industrial kitchen living room coast forest highway inside city mountain open country street tall building office store Dynamic Static

46 feline dog vehicle (Figures from Jia Deng at Stanford and Yangqing Jia at Berkeley).

47 ILSVRC-65

48 Thank you.

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