no code implementations • 13 Apr 2024 • Zhihao Cao, Zidong Wang, Siwen Xie, Anji Liu, Lifeng Fan
Our findings illustrate the potential of AI-imbued assistive robots in improving the well-being of vulnerable groups.
no code implementations • 5 Oct 2023 • Yilue Qian, Peiyu Yu, Ying Nian Wu, Yao Su, Wei Wang, Lifeng Fan
In this paper, we propose an interpretable and generalizable visual planning framework consisting of i) a novel Substitution-based Concept Learner (SCL) that abstracts visual inputs into disentangled concept representations, ii) symbol abstraction and reasoning that performs task planning via the self-learned symbols, and iii) a Visual Causal Transition model (ViCT) that grounds visual causal transitions to semantically similar real-world actions.
1 code implementation • ICCV 2023 • Jiapeng Li, Ping Wei, Wenjuan Han, Lifeng Fan
In this paper, we propose a novel task IntentQA, a special VideoQA task focusing on video intent reasoning, which has become increasingly important for AI with its advantages in equipping AI agents with the capability of reasoning beyond mere recognition in daily tasks.
no code implementations • 28 Nov 2021 • Shuwen Qiu, Sirui Xie, Lifeng Fan, Tao Gao, Jungseock Joo, Song-Chun Zhu, Yixin Zhu
Humans communicate with graphical sketches apart from symbolic languages.
1 code implementation • CVPR 2021 • Lifeng Fan, Shuwen Qiu, Zilong Zheng, Tao Gao, Song-Chun Zhu, Yixin Zhu
By aggregating different beliefs and true world states, our model essentially forms "five minds" during the interactions between two agents.
no code implementations • 25 Apr 2020 • Tao Yuan, Hangxin Liu, Lifeng Fan, Zilong Zheng, Tao Gao, Yixin Zhu, Song-Chun Zhu
Aiming to understand how human (false-)belief--a core socio-cognitive ability--would affect human interactions with robots, this paper proposes to adopt a graphical model to unify the representation of object states, robot knowledge, and human (false-)beliefs.
no code implementations • 20 Apr 2020 • Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.
1 code implementation • ICCV 2019 • Lifeng Fan, Wenguan Wang, Siyuan Huang, Xinyu Tang, Song-Chun Zhu
This paper addresses a new problem of understanding human gaze communication in social videos from both atomic-level and event-level, which is significant for studying human social interactions.
no code implementations • CVPR 2018 • Lifeng Fan, Yixin Chen, Ping Wei, Wenguan Wang, Song-Chun Zhu
We collect a new dataset VideoCoAtt from public TV show videos, containing 380 complex video sequences with more than 492, 000 frames that include diverse social scenes for shared attention study.