LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities

2020-07-31 · ECCV 2020 8 · Baoxiong Jia, Yixin Chen, Siyuan Huang, Yixin Zhu, Song-Chun Zhu

Understanding and interpreting human actions is a long-standing challenge and a critical indicator of perception in artificial intelligence. However, a few imperative components of daily human activities are largely missed in prior literature, including the goal-directed actions, concurrent multi-tasks, and collaborations among multi-agents. We introduce the LEMMA dataset to provide a single home to address these missing dimensions with meticulously designed settings, wherein the number of tasks and agents varies to highlight different learning objectives. We densely annotate the atomic-actions with human-object interactions to provide ground-truths of the compositionality, scheduling, and assignment of daily activities. We further devise challenging compositional action recognition and action/task anticipation benchmarks with baseline models to measure the capability of compositional action understanding and temporal reasoning. We hope this effort would drive the machine vision community to examine goal-directed human activities and further study the task scheduling and assignment in the real world.

📄 PDF Abstract BibTeX arXiv:2007.15781

Code (1)

Buzz-Beater/LEMMA 공식 구현 pytorch

Tasks

Action Recognition Action Understanding Human-Object Interaction DetectionLEMMAScheduling

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