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 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).
    •  
    •  NEWS    MERL Papers and Workshops at CVPR 2024
      Date: June 17, 2024 - June 21, 2024
      Where: Seattle, WA
      MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
      Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.

        CVPR Conference Papers:

        1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks

        This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.

        Paper: https://www.merl.com/publications/TR2024-059
        Project page: https://merl.com/research/highlights/TI2V-Zero

        2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos

        This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.

        Paper: https://www.merl.com/publications/TR2024-040

        3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee

        This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.

        Paper: https://www.merl.com/publications/TR2024-042

        4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi

        Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.

        Paper: https://www.merl.com/publications/TR2024-041

        5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan

        We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.

        Paper: https://www.merl.com/publications/TR2024-043

        CVPR Workshop Papers:

        1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks

        This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.

        Paper: https://www.merl.com/publications/TR2024-045

        2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller

        This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.

        Paper: https://www.merl.com/publications/TR2024-057

        MERL co-organized workshops:

        1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum

        Workshop link: https://marworkshop.github.io/cvpr24/index.html

        2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.

        Workshop link: https://fadetrcv.github.io/2024/

        3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino

        This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.

        Paper: https://www.merl.com/publications/TR2024-062
    •  

    See All News & Events for Kuan-Chuan

  • Research Highlights

  • Internships with Kuan-Chuan

    • CV0050: Internship - Anomaly Localization for Industrial Inspection

      MERL is looking for a self-motivated intern to work on anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but not limited to): cross-view image anomaly localization, how to train one model for multiple views and defect types, how to incorporate large foundation models in image anomaly localization, etc. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) and usage of large foundation models are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

      Required Specific Experience

      • Experience with Python, PyTorch, and large foundation models (e.g. CLIP, ALIGN, etc.).

    • CV0051: Internship - Visual-LiDAR fused object detection and recognition

      MERL is looking for a self-motivated intern to work on visual-LiDAR fused object detection and recognition using computer vision. The relevant topics in the scope include (but not limited to): open-vocabulary visual-LiDAR object detection and recognition, domain adaptation or generalization in visual-LiDAR object detection, data-efficient methods for visual-LiDAR object detection, small object detection with visual-LiDAR input, etc. The candidates with experiences of object recognition in LiDAR are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.

      Required Specific Experience

      • Experience with Python, PyTorch, and datasets with both images and LiDAR (e.g. the nuScenes dataset).

    See All Internships at MERL

  • MERL Publications

    •  Wang, Y., Peng, K.-C., Fu, R., "Towards Zero-shot 3D Anomaly Localization", arXiv, December 2024.
      BibTeX arXiv
      • @article{Wang2024dec,
      • author = {Wang, Yizhou and Peng, Kuan-Chuan and Fu, Raymond}},
      • title = {Towards Zero-shot 3D Anomaly Localization},
      • journal = {arXiv},
      • year = 2024,
      • month = dec,
      • url = {https://arxiv.org/abs/2412.04304}
      • }
    •  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 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}
      • }
    See All MERL Publications for Kuan-Chuan

  • 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
    See All Patents for MERL