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Hank Chi-Hsi Kung I am a visiting researcher at Indiana University Bloomington where I am working with Prof. David Crandall and Prof. Linda Smith. My current research focuses on 3D representation learning in both human and machine intelligence, aiming to reverse-engineer the process by which children develop 3D representation learning. Previously, I was a research assistant at National Chiao Tung University in Taiwan, where I focused on visual compositional representation and self-supervised video representation learning under the guidance of Prof. Yi-Ting Chen and Dr. Yi-Hsuan Tsai. I was a research intern at IBM Thomas J. Watson Research Center. I received my M.Sc from National Tsing-Hua University, where I was supervised by Prof. Che-Rung Lee, and B.Sc from National Taipei University. I am actively seeking a Ph.D. position starting in Fall 2025 Email / Google Scholar / X / Github / CV |
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ResearchMy research goal is to reverse engineer the human cognition. Intuitive Physics World Models, a foundamental componet enabling human to imagining the physical interaction and causality can help machines adapt to novel situations. I aim to build Intuitive Physics World Models to facilitate human-like intelligence. Toward this, learning compositional and augmentable representations is a crucial component as physical interaction and object properties are compositional concepts and can be "reused" to form new patterns and concepts. Moreover, I am fascinated by how humans rapidly learn novel compositions with minimal experience, motivating me to approach learning compositionality through human learning by integrating insights from child development and cognitive sciences.
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News |
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Aug 2024 |
Give a talk at CVGIP 2024! |
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Apr 2024 |
Co-organizing the 3rd ROAD Workshop & Challenge at ECCV 2024! |
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Mar 2024 |
One paper on Visual Action-centric Representation is accepted at CVPR 2024! |
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Feb 2024 |
One paper on Risk Identification is accepted at ICRA 2024! |
Publications |
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Action-slot: Visual Action-centric Representations for Atomic Activity Recognition in Traffic Scenes
Chi-Hsi Kung, Shu-Wei Lu, Yi-Hsuan Tsai, Yi-Ting Chen CVPR, 2024 project page / paper / arxiv / code / TACO dataset We use Action-slot to represent atomic activities. The learned attention can discover and localize atomic activities with only weak video labels and without using any perception module (e.g., object detector). |
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RiskBench: A Scenario-based Benchmark for Risk Identification
Chi-Hsi Kung, Chieh-Chi Yang, Pang-Yuan Pao, Shu-Wei Lu, Pin-Lun Chen, Hsin-Cheng Lu, Yi-Ting Chen ICRA, 2024 project page / video / paper / code / dataset The FIRST benchmark that enables evaluation of various types of risk identification algorithms, namely, rule-based, trajectoy-prediction-based, collision prediction, and behavior-change-based. We also assess the influence of risk identification to the downstream driving task. |
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ADD: A Fine-grained Dynamic Inference Architecture for Semantic Image Segmentation
Chi-Hsi Kung and Che-Rung Lee IROS, 2021 & ACML 2021 MRVC workshop paper / code We use Neural Architecture Search (NAS) to find an optimal structure for dynamic inference on semantic segmentation. |
Conference ReviewerAdvances in Neural Information Processing Systems (2024) The International Conference on Machine Learning (2025) IEEE Conference on Computer Vision and Pattern Recognition (2023-2025) IEEE International Conference on Development and Learning (2024) |
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