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 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
    •  
    •  NEWS    MERL researchers presenting four papers and organizing the VLAR-SMART101 Workshop at ICCV 2023
      Date: October 2, 2023 - October 6, 2023
      Where: Paris/France
      MERL Contacts: Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Brief
      • MERL researchers are presenting 4 papers and organizing the VLAR-SMART-101 workshop at the ICCV 2023 conference, which will be held in Paris, France October 2-6. ICCV is one of the most prestigious and competitive international conferences in computer vision. Details are provided below.

        1. Conference paper: “Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis,” by Nithin Gopalakrishnan Nair, Anoop Cherian, Suhas Lohit, Ye Wang, Toshiaki Koike-Akino, Vishal Patel, and Tim K. Marks

        Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in plug-and-play generation, i.e., using a pre-defined model to guide the generative process. In this paper, we introduce Steered Diffusion, a generalized framework for fine-grained photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation. The key idea is to steer the image generation of the diffusion model during inference via designing a loss using a pre-trained inverse model that characterizes the conditional task. Our model shows clear qualitative and quantitative improvements over state-of-the-art diffusion-based plug-and-play models, while adding negligible computational cost.

        2. Conference paper: "BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus," by Valter Piedade and Pedro Miraldo

        We derive a dynamic Bayesian network that updates individual data points' inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. Our method outperforms the baselines in accuracy while needing less computational time.

        3. Conference paper: "Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes," by Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller

        We present a novel approach to estimating camera rotation in crowded, real-world scenes captured using a handheld monocular video camera. Our method uses a novel generalization of the Hough transform on SO3 to efficiently find the camera rotation most compatible with the optical flow. Because the setting is not addressed well by other data sets, we provide a new dataset and benchmark, with high-accuracy and rigorously annotated ground truth on 17 video sequences. Our method is more accurate by almost 40 percent than the next best method.

        4. Workshop paper: "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection" by Manish Sharma*, Moitreya Chatterjee*, Kuan-Chuan Peng, Suhas Lohit, and Michael Jones

        While state-of-the-art object detection methods for RGB images have reached some level of maturity, the same is not true for Infrared (IR) images. The primary bottleneck towards bridging this gap is the lack of sufficient labeled training data in the IR images. Towards addressing this issue, we present TensorFact, a novel tensor decomposition method which splits the convolution kernels of a CNN into low-rank factor matrices with fewer parameters. This compressed network is first pre-trained on RGB images and then augmented with only a few parameters. This augmented network is then trained on IR images, while freezing the weights trained on RGB. This prevents it from over-fitting, allowing it to generalize better. Experiments show that our method outperforms state-of-the-art.

        5. “Vision-and-Language Algorithmic Reasoning (VLAR) Workshop and SMART-101 Challenge” by Anoop Cherian,  Kuan-Chuan Peng, Suhas Lohit, Tim K. Marks, Ram Ramrakhya, Honglu Zhou, Kevin A. Smith, Joanna Matthiesen, and Joshua B. Tenenbaum

        MERL researchers along with researchers from MIT, GeorgiaTech, Math Kangaroo USA, and Rutgers University are jointly organizing a workshop on vision-and-language algorithmic reasoning at ICCV 2023 and conducting a challenge based on the SMART-101 puzzles described in the paper: Are Deep Neural Networks SMARTer than Second Graders?. A focus of this workshop is to bring together outstanding faculty/researchers working at the intersections of vision, language, and cognition to provide their opinions on the recent breakthroughs in large language models and artificial general intelligence, as well as showcase their cutting edge research that could inspire the audience to search for the missing pieces in our quest towards solving the puzzle of artificial intelligence.

        Workshop link: https://wvlar.github.io/iccv23/
    •  

    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

    •  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}
      • }
    •  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", arXiv, June 2024.
      BibTeX arXiv
      • @article{Cherian2024jun,
      • 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},
      • journal = {arXiv},
      • year = 2024,
      • month = jun,
      • url = {https://arxiv.org/abs/2406.15736}
      • }
    •  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}
      • }
    •  Sharma, M., Chatterjee, M., Peng, K.-C., Lohit, S., Jones, M.J., "Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection", IEEE International Conference on Computer Vision Workshops (ICCV), October 2023, pp. 924-932.
      BibTeX TR2023-125 PDF Presentation
      • @inproceedings{Sharma2023oct,
      • author = {Sharma, Manish and Chatterjee, Moitreya and Peng, Kuan-Chuan and Lohit, Suhas and Jones, Michael J.},
      • title = {Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection},
      • booktitle = {IEEE International Conference on Computer Vision Workshops (ICCV)},
      • year = 2023,
      • pages = {924--932},
      • month = oct,
      • url = {https://www.merl.com/publications/TR2023-125}
      • }
    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