The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary

TL;DR

The 2018 ActivityNet Challenge advanced large-scale activity recognition by hosting diverse tasks on multiple datasets, aiming to improve semantic understanding and captioning of user-generated videos.

Contribution

This paper summarizes the 2018 challenge, introducing new tasks and datasets to enhance activity recognition and video understanding capabilities.

Findings

Hosted six diverse tasks to push semantic video understanding

Integrated three new datasets: Kinetics-600, AVA, and Moments in Time

Facilitated progress in activity detection, captioning, and temporal localization

Abstract

The 3rd annual installment of the ActivityNet Large- Scale Activity Recognition Challenge, held as a full-day workshop in CVPR 2018, focused on the recognition of daily life, high-level, goal-oriented activities from user-generated videos as those found in internet video portals. The 2018 challenge hosted six diverse tasks which aimed to push the limits of semantic visual understanding of videos as well as bridge visual content with human captions. Three out of the six tasks were based on the ActivityNet dataset, which was introduced in CVPR 2015 and organized hierarchically in a semantic taxonomy. These tasks focused on tracing evidence of activities in time in the form of proposals, class labels, and captions. In this installment of the challenge, we hosted three guest tasks to enrich the understanding of visual information in videos. The guest tasks focused on complementary aspects of the activity recognition problem at large scale and involved three challenging and recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT and IBM Research.