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Beyond Nouns Abhinav Gupta and Larry S. Davis University of Maryland, College Park Exploiting Prepositions and Comparative Adjectives for Learning Visual.

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Presentation on theme: "Beyond Nouns Abhinav Gupta and Larry S. Davis University of Maryland, College Park Exploiting Prepositions and Comparative Adjectives for Learning Visual."— Presentation transcript:

1 Beyond Nouns Abhinav Gupta and Larry S. Davis University of Maryland, College Park Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers

2 Beyond Nouns Gupta and Davis What this talk is about Richer linguistic descriptions of images makes learning of object appearance models from weakly labeled images more reliable. Constructing visually-grounded models for parts of speech other than nouns provides contextual models that make labeling new images more reliable. So, this talk is about simultaneous learning of object appearance models and context models for scene analysis. car officer road A officer on the left of car checks the speed of other cars on the road A B Larger (B, A) Larger (tiger, cat) cat tiger BearWaterField A B Larger (A, B) A B Above (A, B)

3 Beyond Nouns Gupta and Davis What this talk is about Prepositions – A preposition usually indicates the temporal, spatial or logical relationship of its object to the rest of the sentence The most common prepositions in English are "about," "above," "across," "after," "against," "along," "among," "around," "at," "before," "behind," "below," "beneath," "beside," "between," "beyond," "but," "by," "despite," "down," "during," "except," "for," "from," "in," "inside," "into," "like," "near," "of," "off," "on," "onto," "out," "outside," "over," "past," "since," "through," "throughout," "till," "to," "toward," "under," "underneath," "until," "up," "upon," "with," "within," and "without” where indicated in bold are the ones (the vast majority) that have clear utility for the analysis of images and video. Comparative adjectives and adverbs– relating to color, size, movement- “larger”, “smaller”, “taller”, “heavier”, “faster”……… This presentation addresses how visually grounded (simple) models for prepositions and comparative adjectives can be acquired and utilized for scene analysis.

4 Beyond Nouns Gupta and Davis Learning Appearances – Weakly Labeled Data Problem: Learning Visual Models for Objects/Nouns Weakly Labeled Data –Dataset of images with associated text or captions Before the start of the debate, Mr. Obama and Mrs. Clinton met with the moderators, Charles Gibson, left, and George Stephanopoulos, right, of ABC News. A officer on the left of car checks the speed of other cars on the road.

5 Beyond Nouns Gupta and Davis Captions - Bag of Nouns Learning Classifiers involves establishing correspondence. road.Aofficer on the left of carchecks the speed of other cars on the officer car road officer car road

6 Beyond Nouns Gupta and Davis Correspondence - Co-occurrence Relationship Bear Water Bear Field Learn Appearances M-step E-step BearWaterField Water Bear Field Bear

7 Beyond Nouns Gupta and Davis Co-occurrence Relationship (Problems) RoadCarRoad Car Road Car RoadCarRoad Car RoadCar Road Car Hypothesis 1 Hypothesis 2 CarRoad

8 Beyond Nouns Gupta and Davis Beyond Nouns – Exploit Relationships Use annotated text to extract nouns and relationships between nouns. road.officer on the left of carchecks the speed of other cars on theA On (car, road) Left (officer, car) car officer road Constrain the correspondence problem using the relationships On (Car, Road) Road Car Road Car More Likely Less Likely

9 Beyond Nouns Gupta and Davis Beyond Nouns - Overview Learn classifiers for both Nouns and Relationships simultaneously. –Classifiers for Relationships based on differential features. Learn priors on possible relationships between pairs of nouns –Leads to better Labeling Performance above (sky, water) above (water, sky) sky water sky water

10 Beyond Nouns Gupta and Davis Related Work Generative Approaches –Duygulu, P., Barnard, K., Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. ECCV (2002) –Barnard, K., Duygulu, P., Freitas, N., Forsyth, D., Blei, D., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research (2003) 1107–1135 –Barnard K and Forsyth D., Learning the semantics of Words and Pictures. ICCV 2001 Relevance Language Models –Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. NIPS (2003) –Feng, S., Manmatha, R., Lavrenko, V.: Multiple Bernoulli relevance models for image and video annotation. CVPR (2004) Classification Based Approaches –Li, J., Wang, J.: Automatic linguistic indexing of pictures by statistical modeling approach. IEEE PAMI (2003) –Andrews, S., Tsochantaridis, I., Hoffman, T.: Support vector machines for multiple-instance learning. NIPS (2002)

11 Beyond Nouns Gupta and Davis Related Work – [1] Model the problem of learning object models as a machine translation problem. –Nouns in the caption (English) have to be translated into visual words (Visual Language). Use statistical machine translation approaches to obtain the correspondence or translation probabilities.

12 Beyond Nouns Gupta and Davis Representation Each image is first segmented into regions. Regions are represented by feature vectors based on: –Appearance (RGB, Intensity) –Shape (Convexity, Moments) Models for nouns are based on features of the regions Relationship models are based on differential features: –Difference of avg. intensity –Difference in location Assumption: Each relationship model is based on one differential feature for convex objects. Learning models of relationships involves feature selection. Each image is also annotated with nouns and a few relationships between those nouns. B B A A B below A

13 Beyond Nouns Gupta and Davis Learning the Model – Chicken Egg Problem Learning models of nouns and relationships requires solving the correspondence problem. To solve the correspondence problem we need some model of nouns and relationships. Chicken-Egg Problem: We treat assignment as missing data and formulate an EM approach. Road Car Road Assignment Problem Learning Problem On (car, road)

14 Beyond Nouns Gupta and Davis EM Approach- Learning the Model E-Step: Compute the noun assignment for a given set of object and relationship models from previous iteration ( ). M-Step: For the noun assignment computed in the E-step, we find the new ML parameters by learning both relationship and object classifiers. For initialization of the EM approach, we can use any image annotation approach with localization such as the translation based model described in [1]. [1] Duygulu, P., Barnard, K., Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. ECCV (2002)

15 Beyond Nouns Gupta and Davis Relationships modeled Most relationships are learned “correctly” –Above, behind, below, left, right, beside, bluer, greener, nearer, more- textured, smaller, larger, brighter But some are associated with the wrong features –In (topological relationships not captured by color, shape and location) –on-top-of –taller (most tall objects are thin and the segmentation algorithm tends to fragment them)

16 Beyond Nouns Gupta and Davis Inference Model Image segmented into regions. Each region represented by a noun node. Every pair of noun nodes is connected by a relationship edge whose likelihood is obtained from differential features. n1n1 n2n2 n3n3 r 12 r 13 r 23

17 Beyond Nouns Gupta and Davis Experimental Evaluation – Corel 5k Dataset Evaluation based on Corel5K dataset [1]. Used 850 training images with tags and manually labeled relationships. Vocabulary of 173 nouns and 19 relationships. We use the same segmentations and feature vector as [1]. Quantitative evaluation of training based on 150 randomly chosen images. Quantitative evaluation of labeling algorithm (testing) was based on 100 test images.

18 Beyond Nouns Gupta and Davis Resolution of Correspondence Ambiguities Evaluate the performance of our approach for resolution of correspondence ambiguities in training dataset. Evaluate performance in terms of two measures [2]: –Range Semantics Counts the “percentage” of each word correctly labeled by the algorithm ‘Sky’ treated the same as ‘Car’ –Frequency Correct Counts the number of regions correctly labeled by the algorithm ‘Sky’ occurs more frequently than ‘Car’ [2] Barnard, K., Fan, Q., Swaminathan, R., Hoogs, A., Collins, R., Rondot, P., Kaufold, J.: Evaluation of localized semantics: data, methodology and experiments. Univ. of Arizona, TR-2005 (2005) Duygulu et. al [1]Our Approach [1] Duygulu, P., Barnard, K., Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. ECCV (2002) below(birds,sun) above(sun, sea) brighter(sun,sea) below(waves,sun) above(statue,rocks); ontopof(rocks, water); larger(water,statue) below(flowers,horses); ontopof(horses,field); below(flowers,foals)

19 Beyond Nouns Gupta and Davis Resolution of Correspondence Ambiguities Compared the performance with IBM Model 1[3] and Duygulu et. al[1] Show importance of prepositions and comparators by bootstrapping our EM- algorithm. (b) Semantic Range (a) Frequency Correct

20 Beyond Nouns Gupta and Davis Examples of labeling test images Duygulu (2002) Our Approach

21 Beyond Nouns Gupta and Davis Evaluation of labeling test images Evaluate the performance of labeling based on annotation from Corel5K dataset Set of Annotations from Ground Truth from Corel Set of Annotations provided by the algorithm Choose detection thresholds to make the number of missed labels approximately equal for two approaches, then compare labeling accuracy

22 Beyond Nouns Gupta and Davis Precision-Recall RecallPrecision [1]Ours[1]Ours Water0.790.900.570.67 Grass0.701.000.840.79 Clouds0.27 0.760.88 Buildings0.250.420.680.80 Sun0.57 0.771.00 Sky0.600.930.981.00 Tree0.660.750.70.75

23 Beyond Nouns Gupta and Davis Experiment – II Used 75 training images from MSRC and Berkeley datasets to evaluate the performance of the system when segmentations are perfect. Vocabulary of 8 nouns and 19 relationships –Use only common words The performance of Duygulu (2002) improves by 5% whereas the performance of our approach improves by ~10%.

24 Beyond Nouns Gupta and Davis Conclusions Richer natural language descriptions of images make it easier to build appearance models for nouns. Models for prepositions and adjectives can then provide us contextual models for labeling new images. Effective man/machine communication requires perceptually grounded models of language. HAL: What’s that, Dave ? HAL: What’s that on the table ? 2001: A Space Odyssey Noun Models Noun and Relationship Models

25 Beyond Nouns Gupta and Davis References [1] Duygulu, P., Barnard, K., Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. ECCV (2002) [2] Barnard, K., Fan, Q., Swaminathan, R., Hoogs, A., Collins, R., Rondot, P., Kaufold, J.: Evaluation of localized semantics: data, methodology and experiments. IJCV 2008 [3] Li, J., Wang, J.: Automatic linguistic indexing of pictures by statistical modeling approach. IEEE PAMI (2003) [4] Barnard, K., Duygulu, P., Freitas, N., Forsyth, D., Blei, D., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research (2003) 1107–1135 [5] Andrews, S., Tsochantaridis, I., Hoffman, T.: Support vector machines for multiple-instance learning. NIPS (2002) [6] Brown, P., Pietra, S., Pietra, V., Mercer, R.: The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics (1993) [7] Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. NIPS (2003) [8] Feng, S., Manmatha, R., Lavrenko, V.: Multiple Bernoulli relevance models for image and video annotation. CVPR (2004) [9] Barnard K and Forsyth D., Learning the semantics of Words and Pictures. ICCV 2001

26 Beyond Nouns Gupta and Davis Thank You !

27 Beyond Nouns Gupta and Davis Limitations and Future Work Assumes One-One relationship between nouns and image segments. –Too much reliance on image segmentation Can these relationships help in improving segmentation ? Use Multiple Segmentations and choose the best segment. On (car, road) Left (tree, road) Above (sky, tree) Larger (Road, Car) Car Tree road

28 Beyond Nouns Gupta and Davis Generative Model CliffSea Cliff Sea Bear beside in C1C1 C2C2 Cliff is beside Sea

29 Beyond Nouns Gupta and Davis Acknowledgements We wish to gratefully acknowledge : Kobus Barnard (Univ. of Arizona) for providing the Corel5K dataset. We also acknowledge funding support provided by: DARPA VACE program

30 Beyond Nouns Gupta and Davis Training Datasets – The Spectrum Human Effort Corel Unlabeled Images

31 Beyond Nouns Gupta and Davis Related Work Generative Models [1,4] –Model joint distribution of nouns and image features. –Barnard et. al [9] presented a generative model for image annotation that induces hierarchical structure from the co-occurrence data. –Shared topic model : Both nouns and visual words are generated based on common topic. θ b w

32 Beyond Nouns Gupta and Davis Related Work – Contd. Classification Approaches [3, 6] –Classifiers learned from positive and negative samples generated based on captions. Relevance Language Models [7, 8] –Find similar images in the training dataset. –Use annotations shared between extracted images to annotate new image.


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