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Natural Language Processing for Action Recognition

Natural Language Processing for Action Recognition. JHU Summer School Evelyne Tzoukermann, Ph.D. Friday, June 11, 2010. What is the role of Natural Language in Action Recognition?. Provide temporal information Where in the video is the action happening? Provide semantic information

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Natural Language Processing for Action Recognition

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  1. Natural Language Processing for Action Recognition JHU Summer School Evelyne Tzoukermann, Ph.D. • Friday, June 11, 2010

  2. What is the role of Natural Language in Action Recognition? • Provide temporal information • Where in the video is the action happening? • Provide semantic information • Parse the phrasal constituents to determine action type and human interaction through objects, instruments, and other contextual information • E.g.: cut potatoes  semantic representation • <instrument> knife • <human interaction> hands • <location> cutting board

  3. Function of Natural Language in Action Recognition? • Facilitate action recognition from the video. • Ground video processing • Extract relevant entities and semantics associated with them • Allow fusion of knowledge from text with action primitives • Leverage already existing techniques and knowledge

  4. Completed • Dataset domains: • Cooking • Crafts • Classification of Actions • Categorization of Actions

  5. Cooking domain • DVD’s: • Cook like a chef • Martha’s Favorite Family Dinners • Joanne Wier’s cooking class • CMU Kitchen dataset • Food Network: 12 consecutive hours of recorded time • PBS Kids: Sprout – 5 shows • URADL: U. of Rochester Activities of Daily Living • 12 activities, 5 individuals, 3 recordings each

  6. Craft domain • PBS Kids: Sprout – over 25 shows

  7. Tuples of Entities • Time stamps for temporal information • Verbs - capture actions • Objects - what is acted upon • Instruments - with what tool • Location – for recognition • Camera position – for scalability

  8. Information Extraction • Extract structured information from unstructured documents Ex: "Yesterday, New-York based Foo Inc. announced their acquisition of Bar Corp.“ • Entity identification and recognition • Goal of IE: allow computation to be performed on unstructured data. • More specific goal: allow logical reasoning to draw inferences based on the logical content of the input data.

  9. Entity Recognition for Video • Can be considered an IE task with a list of entities • Find a tuple or an ordered list with a temporal dimension • Goal of text-based Information Extraction: “Who did what to whom where” • Find the different entities that fill these slots • Goal of video and text IE • Find the temporal, and other entities

  10. Angelina’s Ballet Slippers • Video • Web page

  11. Angelina’s Ballet Slippers Ingredients • 1 red pepper, cut in half with seeds removed • 1⁄2 cup quick cook brown rice • 1⁄2 cup vegetable stock • 1 cup canned mixed vegetables, no added salt • 1⁄4 tsp. black pepper • 1 tsp. chopped fresh parsley • 1 tsp. extra virgin olive oil • 1 lemon • Decorative cabbage • 1⁄4 cup shredded cheddar cheese, divided Supplies • Measuring cups and spoons • Cutting board & knife • Cooking pot • Small cooking pot • Mixing spoons • Slotted spoon • High-sided baking dish • Pastry brush • Large serving plate

  12. Sprout - Alphabet book

  13. Baby Picture Frames

  14. Action Recognition and Complexity Input • transcripts and closed captions • text transcripts alone • list of ingredients and utensils • Evaluation can follow these levels

  15. Sprout – Elmo’s Funny Face Pizza

  16. Sprout – Caillou’s Crunchy Carrot Salad

  17. Martha Stewart Episode 2

  18. Martha Stewart – 191 action verbs

  19. Semantic Categorization of Actions

  20. CMU Kitchen Set - Verbs • take • put • Open • fill • crack • beat • stir • pour • clean • switchon • read • spray • close • walk • wist_on • twist_off

  21. NLP Tools • Part-of-speech tagger or phrase chunker • Dependency parser for Verb-Object relations • We have tuples of Verb, Object, Instrument, Location • Ex: Stir(v)chili(o)with a wooden spoon (instr) in a pot(loc) • Collocations for Instrument and Location • Coocurrence from Google • Ex: “place a wooden spoon across the pot to keep it from boiling” • And more

  22. Ontology • Need to capture: • Concepts • Relationships • Properties • Timestamps (video_name [beg_time, end_time]) • Validation

  23. Ontology for cooking and craft • Need to capture: • Actions • Food – including the state and transformation or • Objects – paper, paper roll, … • Instruments: kitchen utensils, scissors, crayons • Location • Timing • (Recipes)

  24. Ontology • Use of Protégé http://protege.stanford.edu/ • ontology editor and knowledge-base framework. • Knowtator : Protégé plug-in for annotation • can be used for evaluating or • training a variety of NLP systems. • Write a plug-in that takes the output of a syntactic parser and connects it to visual frames

  25. Protégé knowledge-base • class, • Represent the concepts of a domain • organized in a subsumption hierarchy • instance, correspond to individuals of a class • slot, define properties of a class or instance • facet frames constrain the values that slots can have.

  26. Dependency ParserInput Sentence: “Next we need to open the can of veggies” ROOT [next-1] ( SBAR [next-1] ( next-1(Next)/IN S [need-6] ( NP [we-3] ( we-3/PRP ) VP [need-6] ( need-6/VBP S [to-8] ( VP [to-8] ( to-8/TO VP [open-10] ( open-10/VB NP [can-14] ( NP [can-14] ( the-12/DT can-14/NN ) PP [of-17] ( of-17/IN NP [veggy-19] ( veggy-19(veggies)/NNS ) )

  27. Dependency ParserInput Sentence: “Next we need to open the can of veggies” ROOT [next-1] ( SBAR [next-1] ( next-1(Next)/IN S [need-6] ( NP [we-3] ( we-3/PRP ) VP [need-6] ( need-6/VBP S [to-8] ( VP [to-8] ( to-8/TO VP [open-10] ( open-10/VB NP [can-14] ( NP [can-14] ( the-12/DT can-14/NN ) PP [of-17] ( of-17/IN NP [veggy-19] ( veggy-19(veggies)/NNS ) )

  28. Action concept and relations with other concepts Action Verb Object Human Interaction Instrument Location Time Vn,t1,t2

  29. Knowtator: Annotation Plug-in • General purpose annotation tool • Facilitates creation of training and evaluation corpora for language processing tasks • Ease of use • Straightforward to incorporate domain knowledge

  30. Knowtator: an example

  31. Processes Ontology Creation Syntactic Parser Ontology Annotation Corpus enrichment using collocations

  32. Related Research • Ontology and cooking • Parsing “restricted” languages • Connecting text with images

  33. Related Research • Dina Demner-Fushman, SameerAntani, Matthew Simpson, George R. Thoma “Annotation and retrieval of clinically relevant images”, 2009 • Ricardo Ribeiro, Fernando Batista, Joana Paulo Pardal, Nuno J. Mamede, and H. Sofia Pinto “Cooking an Ontology?”, 2008 • Fernando Batista, Joana Paulo, NunoMamede, Paula Vaz, Ricardo Ribeiro “Ontology construction: cooking domain”, 2006 • Joana Paulo Pardal, “Dynamic Use of Ontologies in Dialogue Systems”, 2009

  34. Related Research • Mutsuo Sano, Ichiro Ide, Kenzaburo Miyawaki “Overview of the ACM Multimedia 2009 Workshop on Multimedia for Cooking and Eating Activities (CEA’09)” • Keigo Kitamura Toshihiko Yamasaki KiyoharuAizawa “FoodLog: Capture, Analysis and Retrieval of Personal Food Images via Web”, 2009 distinguishes food images from other images • Dan Tasse and Noah Smith (CMU) SOUR CREAM:Toward Semantic Processing of Recipes, 2008 • new techniques for semantic parsing by focusing on the domain of cooking recipes • first order logic

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