As a joint activity with the research teams from the Chinese Academy of Sciences, Academia Sinica, and Microsoft Research Asia, we are releasing a large-scale social media dataset for sociological understanding and predictions, namely Social Media Prediction (SMP) dataset, with over 850K posts and 80K users in total. Our goal is to make the SMP dataset as varied and rich as possible to thoroughly represent the social media “world”. Particularly, we aim to record the dynamic variance of social media data. For example, the social media posts in the dataset are obtained with temporal information to preserve the continuity of post sequences.
The contestants are asked to develop video to language systems based on the MSR-VTT dataset provided by the Challenge (as training data) and any other public/private data to recognize a wide range of object, scene, event, etc., in the images/videos. For the evaluation purpose, a contesting system is asked to produce at least one sentence of the test videos. The accuracy will be evaluated against human pre-generated sentence(s) during evaluation stage.
One of the biggest challenges of automated driving is to accurately determine the location of a vehicle relative to the roadway. Equipped with GPS, in-vehicle sensors (cameras), and a highly accurate 3D map, an automated driving system must be reliable, even under harsh conditions, due to GPS denial or imprecision, in-vehicle sensor malfunction, heavy occlusions, poor lighting, and inclement weather. Lane level localization on a 3D map allows the vehicle to function reliably in such conditions.
ACM Multimedia 2017 Grand Challenge
Chair
University of North Carolina
Submission
Submission site: https://easychair.org/conferences/?conf=mmw17
Social Media Prediction (SMP)
As a joint activity with the research teams from the Chinese Academy of Sciences, Academia Sinica, and Microsoft Research Asia, we are releasing a large-scale social media dataset for sociological understanding and predictions, namely Social Media Prediction (SMP) dataset, with over 850K posts and 80K users in total. Our goal is to make the SMP dataset as varied and rich as possible to thoroughly represent the social media “world”. Particularly, we aim to record the dynamic variance of social media data. For example, the social media posts in the dataset are obtained with temporal information to preserve the continuity of post sequences.
VIEW MORE
The 2nd MSR Video to Language Challenge
The contestants are asked to develop video to language systems based on the MSR-VTT dataset provided by the Challenge (as training data) and any other public/private data to recognize a wide range of object, scene, event, etc., in the images/videos. For the evaluation purpose, a contesting system is asked to produce at least one sentence of the test videos. The accuracy will be evaluated against human pre-generated sentence(s) during evaluation stage.
VIEW MORE
Lane Level Localization on a 3D Map
One of the biggest challenges of automated driving is to accurately determine the location of a vehicle relative to the roadway. Equipped with GPS, in-vehicle sensors (cameras), and a highly accurate 3D map, an automated driving system must be reliable, even under harsh conditions, due to GPS denial or imprecision, in-vehicle sensor malfunction, heavy occlusions, poor lighting, and inclement weather. Lane level localization on a 3D map allows the vehicle to function reliably in such conditions.
VIEW MORE