In addition to the above folders, there is a MOVIE_NAME.entity.types.txt
file for each movie which shows the type of each key entity in the movie (persons, locations, animals, concepts).
Each entity name should have corresponding image examples in the "images" folder.
Also, queries at the movie and scene level use those exact same names to refer to
persons or entities (locations) in the movie.
All relations between entities (person to person, person to location) are to be selected from the relationship ontology provided:
Please see the ontology of relationships, interactions, locations, and sentiments:
https://www-nlpir.nist.gov/projects/trecvid/dvu/dvu.development.dataset/vocab.dvu.json
The relationships in this json file includes all relationships in the movie-level relationships ontology as well as scene-level interactions and sentiments.
Please note that the use of the term "entity" in this file refers to a location in the above entity.types files.
Please follow the provided samples of XML and DTD files to correctly format your run submissions.
Fill in the part of graph question.
The task for systems will be to identify the person / entity labelled Unknown_#.
All of Unknown's relations with other people / entities / concepts are listed.
In cases where one of these related nodes occurs more than once in the part of
graph questions, That node's name has been replaced with <BLANK>. Therefore any
nodes labelled <BLANK> are guaranteed to be one of the nodes named in this group of questions.
The subject type will always be the source person we are asking about. The predicate
will always be that person’s relation with another person, entity, or concept.
The subject in this question always contains the Unknown you are being asked to identify.
Multiple choice questions.
The task for systems will be to identify the correct answer for Unknown out of the possible answers provided.
The questions in this query type is natural language and is not structured by subject, predicate and object.
Find the unique scene.
Given a full, inclusive list of interactions unique to a specific scene in the movie,
teams should find which scene this is.
The subject type will always be the scene needed to be identified:
Teams will return the scene id, based on the segmented scenes reference files (csv files)
and/or segmented movie shots.
Find next interaction in scene X between person Y and person Z
Given a specific scene X and a specific interaction between person Y and person Z,
participants will be asked to select either the next interaction between person Y and
Person Z in scene X or X + N, from a set of multiple choice options of different interactions.
The two persons in this question are always subjects and objects. While the interaction is the predicate.
Find previous interaction in scene X between person Y and person Z Given a specific scene X and a specific interaction between person Y and person Z, participants will be asked to select either the previous interaction between person Y and Person Z in scene X or X - N, from a set of multiple choice options of different interactions. The two persons in this question are always subjects and objects. While the interaction is the predicate.
Given a list of natural language descriptions of scenes and a list of scene numbers, Participants will be asked to select the best scene (from the provided list) that best matches the given description in the query.
Given a specific movie scene and a set of possible sentiments, participants will be asked to
select the best sentiment (from the provided list) that best matches the given scene in the query.