This challenge is the 3rd annual installment of the ActivityNet Large-Scale Activity Recognition Challenge, which was first hosted during CVPR 2016. It focuses on the recognition of daily life, high-level, goal-oriented activities from user-generated videos as those found in internet video portals.
We are proud to announce that this year the challenge will hosts seven diverse tasks which aim to push the limits of semantic visual understanding of videos as well as bridging visual content with human captions. Three out of the seven tasks in the challenge are based on the ActivityNet dataset, which was introduced in CVPR 2015 and organized hierarchically in a semantic taxonomy. These tasks focus on trace evidence of activities in time in the form of actionness/proposals, class labels, and captions.
In this installment of the challenge, we will host four guest tasks which enrich the understanding of visual information in videos. These tasks focus on complementary aspects of the activity recognition problem at large scale and involve challenging and recently compiled activity/action datasets, including Kinetics (Google DeepMind), AVA (Berkeley and Google), SoA (Facebook), and Moments in Time (MIT and IBM Research).