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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 2: AAAI-22 Technical Tracks 2

ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting

February 1, 2023

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Authors

Yuke Li

UC Berkeley


Pin Wang

University of California, Berkeley


Lixiong Chen

University of Oxford


Zheng Wang

Wuhan University


Ching-Yao Chan

UC Berkeley


DOI:

10.1609/aaai.v36i2.20038


Abstract:

This paper describes an energy-based learning method that predicts the activities of multiple agents simultaneously. It aims to forecast both upcoming actions and paths of all agents in a scene based on their past activities, which can be jointly formulated by a probabilistic model over time. Learning this model is challenging because: 1) it has a large number of time-dependent variables that must scale with the forecast horizon and the number of agents; 2) distribution functions have to contain multiple modes in order to capture the spatio-temporal complexities of each agent's activities. To address these challenges, we put forth a novel Energy-based Learning approach for Multi-Agent activity forecasting (ELMA) to estimate this complex model via maximum log-likelihood estimation. Specifically, by sampling from a sequence of factorized marginalized multi-model distributions, ELMA generates most possible future actions efficiently. Moreover, by graph-based representations, ELMA also explicitly resolves the spatio-temporal dependencies of all agents' activities in a single pass. Our experiments on two large-scale datasets prove that ELMA outperforms recent leading studies by an obvious margin.

Topics: AAAI

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HOW TO CITE:

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting Proceedings of the AAAI Conference on Artificial Intelligence (2022) 1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting AAAI 2022, 1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan (2022). ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan. ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan. 2022. ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting. "Proceedings of the AAAI Conference on Artificial Intelligence". 1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan. (2022) "ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting", Proceedings of the AAAI Conference on Artificial Intelligence, p.1482-1490

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan, "ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting", AAAI, p.1482-1490, 2022.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan. "ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan. "ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 1482-1490.

Yuke Li||Pin Wang||Lixiong Chen||Zheng Wang||Ching-Yao Chan. ELMA: Energy-Based Learning for Multi-Agent Activity Forecasting. AAAI[Internet]. 2022[cited 2023]; 1482-1490.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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101, Palo Alto, California 94303 All Rights Reserved

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