-------------------------------------------------- MediaEval 2019: Multimedia Research Challenges Register now: Data releases are beginning https://docs.google.com/forms/d/e/1FAIpQLSfxS4LPBhLQUTXSPT5vogtiSy7BuAKrPs6u6pZXcSV1Xs7XEQ/viewform Last day for regular registration: 30 June http://multimediaeval.org/mediaeval2019 --------------------------------------------------
MediaEval (Benchmarking Initiative for Multimedia Evaluation) offers shared-tasks to the multimedia research community involving images, text, video, music and speech. The tasks address cutting-edge multimedia challenges (multimedia mining, retrieval, analysis, access and exploration) with a clear human or social aspect. Our larger aim is to promote reproducible research that makes multimedia a positive force for society. Further information and the link for registration to participate in the tasks is available at: http://multimediaeval.org/mediaeval2019 Short descriptions of the 2019 tasks are below. The MediaEval 2019 Workshop will take place 27-29 October 2019 near Nice, France. (The workshop is scheduled so that participants can combine the workshop with attendance at ACM Multimedia https://www.acmmm.org/2019 in one trip, if they wish.) #Emotion and Theme recognition in music using Jamendo# Recognize emotions and themes conveyed in music recordings (large-scale data set). http://multimediaeval.org/mediaeval2019/music #Eyes and Ears Together: Multimodal coreference resolution# Analyze videos to predict bounding boxes corresponding to nouns and pronouns in the videos’ speech transcripts. http://multimediaeval.org/mediaeval2019/eyesears #GameStory: Video Game Analytics Challenge# Analyze game streams (including audio and video streams, commentaries, game data and statistics, interaction traces, viewer-to-viewer communication) to carry out synchronization and event detection. http://multimediaeval.org/mediaeval2019/gamestory #Insight for Wellbeing: Multimodal personal health lifelog data analysis# Analyze lifelogs (lifelog images, user-contributed tags, sensor readings, weather/pollution information) to automatically make well-being related predictions. http://multimediaeval.org/mediaeval2019/wellbeing #Medico Medical Multimedia# Analyze a multimodal dataset (videos, analysis data, study participant data) to make predictions related to sperm quality. http://multimediaeval.org/mediaeval2019/medico #Multimedia Recommender Systems# Participants can choose between one of two tasks that investigate the use of multimedia content for recommendation. http://multimediaeval.org/mediaeval2019/mmrecsys #Multimedia Satellite Task: Flood Severity Estimation# Analyze news reports (images/text) and/or satellite images for information important for disaster management. http://multimediaeval.org/mediaeval2019/multimediasatellite #No-audio Multimodal Speech Detection# Participants receive videos (top view) and sensor readings (acceleration and proximity) of people having conversations in a natural social setting and are required to detect speaking turns. http://multimediaeval.org/mediaeval2019/speakerturns #Pixel Privacy# Participants receive a set of images and are required to enhance them in a way that blocks automatic inference of sensitive information, while preserving image appeal. See: https://youtu.be/zShHPVOA070. http://multimediaeval.org/mediaeval2019/pixelprivacy #Predicting Media Memorability# Given a data set of multimedia content (images and/or videos) and associated memorability annotations, automatically train a system to predict memorability. http://multimediaeval.org/mediaeval2019/memorability #Scene Change (Brave New Task)# Automatically create fun faux photo’s, composite images that fool you at first, but can be identified as an imitation on closer inspection. http://multimediaeval.org/mediaeval2019/scenechange #Sports Video Annotation: Detection of Strokes in Table Tennis (Brave New Task)# Automatically classify strokes in videos of table tennis. http://multimediaeval.org/mediaeval2019/sports #NewsFire: Discovering the triggers for viral news stories (Task force)# Participants receive a large corpus of news stories and social media posts (text and images) and are required to build a system that detects the original triggers of news that spread with a viral or wildfire pattern. For more information on the mission of MediaEval check out the videos and proceedings from previous workshops, e.g., http://multimediaeval.org/mediaeval2018 If you have further questions, please contact: Martha Larson m.a.lar...@tudelft.nl On behalf of the MediaEval Community Council, Prof. Bogdan IONESCU ETTI - University Politehnica of Bucharest http://campus.pub.ro/lab7/bionescu/ _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
