Kuan-Chuan Peng

Kuan-Chuan Peng
Kuan-Chuan's Quick Links

  • Biography

    Before joining MERL, he was a Research Scientist (2016-2018) and Staff Scientist (2019) at Siemens Corporate Technology. His PhD research focuses on solving abstract tasks in computer vision using convolutional neural networks. In addition to his PhD, he received a bachelor's degree in Electrical Engineering and an MS degree in Computer Science and Information Engineering from National Taiwan University in 2009 and 2012 respectively. His research interests include incremental learning, developing practical solutions given biased or scarce data, and fundamental computer vision and machine learning problems.

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at AAAI 2025
      Date: February 25, 2025 - March 4, 2025
      Where: The Association for the Advancement of Artificial Intelligence (AAAI)
      MERL Contacts: Ankush Chakrabarty; Toshiaki Koike-Akino; Jing Liu; Kuan-Chuan Peng; Diego Romeres; Ye Wang
      Research Areas: Artificial Intelligence, Machine Learning, Optimization
      Brief
      • MERL researchers presented 2 conference papers, 2 workshop papers, and co-organized 1 workshop at the AAAI 2025 conference, which was held in Philadelphia from Feb. 25 to Mar. 4, 2025. AAAI is one of the most prestigious and competitive international conferences in artificial intelligence (AI). Details of MERL contributions are provided below.

        - AAAI Papers in Main Tracks:

        1. "Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage" by M.R.U. Rashid, J. Liu, T. Koike-Akino, Y. Wang, and S. Mehnaz. [Oral Presentation]

        This work proposes a novel unlearning-based model poisoning method that amplifies privacy breaches during fine-tuning. Extensive empirical studies show the proposed method’s efficacy on both membership inference and data extraction attacks. The attack is stealthy enough to bypass detection based defenses, and differential privacy cannot effectively defend against the attacks without significantly impacting model utility.

        Paper: https://www.merl.com/publications/TR2025-017

        2. "User-Preference Meets Pareto-Optimality: Multi-Objective Bayesian Optimization with Local Gradient Search" by J.H.S. Ip, A. Chakrabarty, A. Mesbah, and D. Romeres. [Poster Presentation]

        This paper introduces a sample-efficient multi-objective Bayesian optimization method that integrates user preferences with gradient-based search to find near-Pareto optimal solutions. The proposed method achieves high utility and reduces distance to Pareto-front solutions across both synthetic and real-world problems, underscoring the importance of minimizing gradient uncertainty during gradient-based optimization. Additionally, the study introduces a novel utility function that respects Pareto dominance and effectively captures diverse user preferences.

        Paper: https://www.merl.com/publications/TR2025-018

        - AAAI Workshop Papers:

        1. "Quantum Diffusion Models for Few-Shot Learning" by R. Wang, Y. Wang, J. Liu, and T. Koike-Akino.

        This work presents the quantum diffusion model (QDM) as an approach to overcome the challenges of quantum few-shot learning (QFSL). It introduces three novel algorithms developed from complementary data-driven and algorithmic perspectives to enhance the performance of QFSL tasks. The extensive experiments demonstrate that these algorithms achieve significant performance gains over traditional baselines, underscoring the potential of QDM to advance QFSL by effectively leveraging quantum noise modeling and label guidance.

        Paper: https://www.merl.com/publications/TR2025-025

        2. "Quantum Implicit Neural Compression", by T. Fujihashi and T., Koike-Akino.

        This work introduces a quantum counterpart of implicit neural representation (quINR) which leverages the exponentially rich expressivity of quantum neural networks to improve the classical INR-based signal compression methods. Evaluations using some benchmark datasets show that the proposed quINR-based compression could improve rate-distortion performance in image compression compared with traditional codecs and classic INR-based coding methods.

        Paper: https://www.merl.com/publications/TR2025-024

        - AAAI Workshops Contributed by MERL:

        1. "Scalable and Efficient Artificial Intelligence Systems (SEAS)"

        K.-C. Peng co-organized this workshop, which offers a timely forum for experts to share their perspectives in designing and developing robust computer vision (CV), machine learning (ML), and artificial intelligence (AI) algorithms, and translating them into real-world solutions.

        Workshop link: https://seasworkshop.github.io/aaai25/index.html

        2. "Quantum Computing and Artificial Intelligence"

        T. Koike-Akino served a session chair of Quantum Neural Network in this workshop, which focuses on seeking contributions encompassing theoretical and applied advances in quantum AI, quantum computing (QC) to enhance classical AI, and classical AI to tackle various aspects of QC.

        Workshop link: https://sites.google.com/view/qcai2025/
    •  
    •  NEWS    MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
    •  

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  • Research Highlights

  • MERL Publications

    •  Wang, Y., Peng, K.-C., Fu, R., "Towards Zero-shot 3D Anomaly Localization", IEEE Winter Conference on Applications of Computer Vision (WACV), February 2025.
      BibTeX TR2025-020 PDF Video Presentation
      • @inproceedings{Wang2025feb2,
      • author = {Wang, Yizhou and Peng, Kuan-Chuan and Fu, Raymond},
      • title = {{Towards Zero-shot 3D Anomaly Localization}},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2025,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2025-020}
      • }
    •  Cherian, A., Peng, K.-C., Lohit, S., Matthiesen, J., Smith, K., Tenenbaum, J.B., "Evaluating Large Vision-and-Language Models on Children’s Mathematical Olympiads", Advances in Neural Information Processing Systems (NeurIPS), November 2024.
      BibTeX TR2024-160 PDF Video Presentation
      • @inproceedings{Cherian2024nov,
      • author = {Cherian, Anoop and Peng, Kuan-Chuan and Lohit, Suhas and Matthiesen, Joanna and Smith, Kevin and Tenenbaum, Joshua B.},
      • title = {{Evaluating Large Vision-and-Language Models on Children’s Mathematical Olympiads}},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = nov,
      • url = {https://www.merl.com/publications/TR2024-160}
      • }
    •  Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection", European Conference on Computer Vision (ECCV), Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O. and Sattler, T. and Varol, G., Eds., DOI: 10.1007/​978-3-031-73347-5_27, September 2024, pp. 475-491.
      BibTeX TR2024-130 PDF Video Presentation
      • @inproceedings{Hegde2024sep,
      • author = {Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.},
      • title = {{Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection}},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2024,
      • editor = {Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O. and Sattler, T. and Varol, G.},
      • pages = {475--491},
      • month = sep,
      • publisher = {Springer},
      • doi = {10.1007/978-3-031-73347-5_27},
      • issn = {0302-9743},
      • isbn = {978-3-031-73346-8},
      • url = {https://www.merl.com/publications/TR2024-130}
      • }
    •  Ho, C.-H., Peng, K.-C., Vasconcelos, N., "Long-Tailed Anomaly Detection with Learnable Class Names", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Farhadi, A. and Crandall, D. and Sato, I. and Wu, J. and Pless, R. and Akata, Z., Eds., DOI: 10.1109/​CVPR52733.2024.01182, June 2024, pp. 12435-12446.
      BibTeX TR2024-040 PDF Video Data Presentation
      • @inproceedings{Ho2024jun,
      • author = {Ho, Chih-Hui and Peng, Kuan-Chuan and Vasconcelos, Nuno},
      • title = {{Long-Tailed Anomaly Detection with Learnable Class Names}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2024,
      • editor = {Farhadi, A. and Crandall, D. and Sato, I. and Wu, J. and Pless, R. and Akata, Z.},
      • pages = {12435--12446},
      • month = jun,
      • publisher = {IEEE},
      • doi = {10.1109/CVPR52733.2024.01182},
      • issn = {2575-7075},
      • isbn = {979-8-3503-5300-6},
      • url = {https://www.merl.com/publications/TR2024-040}
      • }
    •  Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Multimodal 3D Object Detection on Unseen Domains", arXiv, April 2024.
      BibTeX arXiv
      • @article{Hegde2024apr,
      • author = {Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.},
      • title = {{Multimodal 3D Object Detection on Unseen Domains}},
      • journal = {arXiv},
      • year = 2024,
      • month = apr,
      • url = {https://arxiv.org/abs/2404.11764}
      • }
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  • Other Publications

    •  Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu and Rama Chellappa, "Learning without Memorizing", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
      BibTeX
      • @Inproceedings{Dhar_CVPR19,
      • author = {Dhar, Prithviraj and Singh, Rajat Vikram and Peng, Kuan-Chuan and Wu, Ziyan and Chellappa, Rama},
      • title = {Learning without Memorizing},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2019
      • }
    •  Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst and Yun Fu, "Guided Attention Inference Network", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019.
      BibTeX
      • @Article{Li_TPAMI19,
      • author = {Li, Kunpeng and Wu, Ziyan and Peng, Kuan-Chuan and Ernst, Jan and Fu, Yun},
      • title = {Guided Attention Inference Network},
      • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
      • year = 2019,
      • publisher = {IEEE}
      • }
    •  Lezi Wang, Ziyan Wu, Srikrishna Karanam, Kuan-Chuan Peng, Rajat Vikram Singh, Bo Liu and Dimitris N. Metaxas, "Sharpen Focus: Learning with Attention Separability and Consistency", IEEE International Conference on Computer Vision (ICCV), 2019.
      BibTeX
      • @Inproceedings{Wang_ICCV19,
      • author = {Wang, Lezi and Wu, Ziyan and Karanam, Srikrishna and Peng, Kuan-Chuan and Singh, Rajat Vikram and Liu, Bo and Metaxas, Dimitris N.},
      • title = {Sharpen Focus: Learning with Attention Separability and Consistency},
      • booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      • year = 2019
      • }
    •  Yunye Gong, Srikrishna Karanam, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst and Peter C. Doerschuk, "Learning Compositional Visual Concepts with Mutual Consistency", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
      BibTeX
      • @Inproceedings{Gong_CVPR18,
      • author = {Gong, Yunye and Karanam, Srikrishna and Wu, Ziyan and Peng, Kuan-Chuan and Ernst, Jan and Doerschuk, Peter C.},
      • title = {Learning Compositional Visual Concepts with Mutual Consistency},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2018
      • }
    •  Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst and Yun Fu, "Tell Me Where to Look: Guided Attention Inference Network", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
      BibTeX
      • @Inproceedings{Li_CVPR18,
      • author = {Li, Kunpeng and Wu, Ziyan and Peng, Kuan-Chuan and Ernst, Jan and Fu, Yun},
      • title = {Tell Me Where to Look: Guided Attention Inference Network},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2018
      • }
    •  Kuan-Chuan Peng, Ziyan Wu and Jan Ernst, "Zero-Shot Deep Domain Adaptation", European Conference on Computer Vision (ECCV), 2018.
      BibTeX
      • @Inproceedings{Peng_ECCV18,
      • author = {Peng, Kuan-Chuan and Wu, Ziyan and Ernst, Jan},
      • title = {Zero-Shot Deep Domain Adaptation},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2018
      • }
    •  Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik and Andrew C. Gallagher, "Where Do Emotions Come from? Predicting the Emotion Stimuli Map", IEEE International Conference on Image Processing (ICIP), 2016.
      BibTeX
      • @Inproceedings{Peng_ICIP16,
      • author = {Peng, Kuan-Chuan and Chen, Tsuhan and Sadovnik, Amir and Gallagher, Andrew C.},
      • title = {Where Do Emotions Come from? Predicting the Emotion Stimuli Map},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2016
      • }
    •  Kuan-Chuan Peng and Tsuhan Chen, "Toward Correlating and Solving Abstract Tasks Using Convolutional Neural Networks", IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
      BibTeX
      • @Inproceedings{Peng_WACV16,
      • author = {Peng, Kuan-Chuan and Chen, Tsuhan},
      • title = {Toward Correlating and Solving Abstract Tasks Using Convolutional Neural Networks},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2016
      • }
    •  Kuan-Chuan Peng, Tsuhan Chen, Amir Sadovnik and Andrew C. Gallagher, "A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
      BibTeX
      • @Inproceedings{Peng_CVPR15,
      • author = {Peng, Kuan-Chuan and Chen, Tsuhan and Sadovnik, Amir and Gallagher, Andrew C.},
      • title = {A Mixed Bag of Emotions: Model, Predict, and Transfer Emotion Distributions},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2015
      • }
    •  Kuan-Chuan Peng and Tsuhan Chen, "Cross-layer Features in Convolutional Neural Networks for Generic Classification Tasks", IEEE International Conference on Image Processing (ICIP), 2015.
      BibTeX
      • @Inproceedings{Peng_ICIP15,
      • author = {Peng, Kuan-Chuan and Chen, Tsuhan},
      • title = {Cross-layer Features in Convolutional Neural Networks for Generic Classification Tasks},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2015
      • }
    •  Kuan-Chuan Peng and Tsuhan Chen, "A Framework of Extracting Multi-scale Features Using Multiple Convolutional Neural Network", IEEE International Conference on Multimedia and Expo (ICME), 2015.
      BibTeX
      • @Inproceedings{Peng_ICME15,
      • author = {Peng, Kuan-Chuan and Chen, Tsuhan},
      • title = {A Framework of Extracting Multi-scale Features Using Multiple Convolutional Neural Network},
      • booktitle = {IEEE International Conference on Multimedia and Expo (ICME)},
      • year = 2015
      • }
    •  Kuan-Chuan Peng, Kolbeinn Karlsson, Tsuhan Chen, Dongqing Zhang and Hong Heather Yu, "A Framework of Changing Image Emotion Using Emotion Prediction", IEEE International Conference on Image Processing (ICIP), 2014.
      BibTeX
      • @Inproceedings{Peng_ICIP14,
      • author = {Peng, Kuan-Chuan and Karlsson, Kolbeinn and Chen, Tsuhan and Zhang, Dongqing and Yu, Hong Heather},
      • title = {A Framework of Changing Image Emotion Using Emotion Prediction},
      • booktitle = {IEEE International Conference on Image Processing (ICIP)},
      • year = 2014
      • }
    •  Kuan-Chuan Peng and Tsuhan Chen, "Incorporating Cloud Distribution in Sky Representation", IEEE International Conference on Computer Vision (ICCV), 2013.
      BibTeX
      • @Inproceedings{Peng_ICCV13,
      • author = {Peng, Kuan-Chuan and Chen, Tsuhan},
      • title = {Incorporating Cloud Distribution in Sky Representation},
      • booktitle = {IEEE International Conference on Computer Vision (ICCV)},
      • year = 2013
      • }

  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "Contactless Elevator Service for an Elevator Based on Augmented Datasets"
      Inventors: Sahinoglu, Zafer; Peng, Kuan-Chuan; Sullivan, Alan; Yerazunis, William S.
      Patent No.: 12,071,323
      Issue Date: Aug 27, 2024
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