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Zhimin Li

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3 papers

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EAAI Journal 2024 Journal Article

A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers

Zhimin Li
Ruilin Li
Liqiang Yuan
Jian Cui
Fan Li

Vessel Traffic Controllers (VTCs) play a crucial role in ensuring safe navigation by maintaining a high level of vigilance. Eye-tracking has been identified as one of the most popular bio-signals for vigilance prediction. However, the existing studies on eye-tracking-based vigilance prediction usually utilized a limited set of features for analysis. A comprehensive analysis of various eye-tracking features and a unified model with general high performance remains a gap in current research. To address this issue, this study introduces a benchmarking framework for eye-tracking-based vigilance prediction and feature analysis. In the framework, a hierarchical analysis method is proposed, which explores a diverse set of eye-tracking features at both individual and group levels. Additionally, a vigilance ensemble model is proposed. Model interpretation is carried out by using the Shapley additive explanation (SHAP) method. The results highlight the superior performance of the proposed ensemble model. Moreover, a comparative analysis between professional VTCs and novices is performed. High-importance features and feature groups are identified separately. Upon comparison, it is also found that professionals demonstrate efficient attention allocation, while novices exhibit unique patterns influenced by their exploration strategies and scattered attention. The conclusions can serve as a valuable reference for maritime practitioners and provide insights into vigilance prediction across various domains.

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IJCAI Conference 2024 Conference Paper

Digital Avatars: Framework Development and Their Evaluation

Timothy Rupprecht
Sung-En Chang
Yushu Wu
Lei Lu
Enfu Nan
Chih-hsiang Li
Caiyue Lai
Zhimin Li

We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we present Crowd Vote - an adaptation of Crowd Score that allows for judges to elect a large language model (LLM) candidate over competitors answering the same or similar prompts. To visualize the responses of our LLM, and the effectiveness of our prompting strategy we propose an end-to-end framework for creating high-fidelity artificial intelligence (AI) driven digital avatars. This pipeline effectively captures an individual's essence for interaction and our streaming algorithm delivers a high-quality digital avatar with real-time audio-video streaming from server to mobile device. Both our visualization tool, and our Crowd Vote metrics demonstrate our AI driven digital avatars have state-of-the-art humor, authenticity, and favorability outperforming all competitors and baselines. In the case of our Donald Trump and Joe Biden avatars, their authenticity and favorability are rated higher than even their real-world equivalents.

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AAAI Conference 2022 Conference Paper

Improving Human-Object Interaction Detection via Phrase Learning and Label Composition

Zhimin Li
Cheng Zou
Yu Zhao
Boxun Li
Sheng Zhong

Human-Object Interaction (HOI) detection is a fundamental task in high-level human-centric scene understanding. We propose PhraseHOI, containing a HOI branch and a novel phrase branch, to leverage language prior and improve relation expression. Specifically, the phrase branch is supervised by semantic embeddings, whose ground truths are automatically converted from the original HOI annotations without extra human efforts. Meanwhile, a novel label composition method is proposed to deal with the long-tailed problem in HOI, which composites novel phrase labels by semantic neighbors. Further, to optimize the phrase branch, a loss composed of a distilling loss and a balanced triplet loss is proposed. Extensive experiments are conducted to prove the effectiveness of the proposed PhraseHOI, which achieves significant improvement over the baseline and surpasses previous state-of-the-art methods on Full and NonRare on the challenging HICO-DET benchmark.

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