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CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion
Yaowei Guo;Jiazheng Xing;Xiaojun Hou;Shuo Xin;Juntao Jiang;Demetri Terzopoulos;Chenfanfu Jiang;Yong Liu
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Video summarization, by selecting the most informative and/or user-relevant parts of original videos to create concise summary videos, has high research value and consumer demand in today’s video proliferation era. Multi-modal video summarization that accomodates user input has become a research hotspot. However, current multi-modal video summarization methods suffer from two limitations. First, e...Show More
CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion
Yaowei Guo;Jiazheng Xing;Xiaojun Hou;Shuo Xin;Juntao Jiang;Demetri Terzopoulos;Chenfanfu Jiang;Yong Liu
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Folding
Andrew Choi;Dezhong Tong;Demetri Terzopoulos;Jungseock Joo;Mohammad Khalid Jawed
Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as imitation learning, have been used to address deformable material manipulation. These approaches lack generality and often suffer critical failure from a simple switch of material, geometric, and/or environmental (e.g., ...Show More
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision
Feng Luo;Ling Liu;G. Geoff Wang;Vijay Kumar;Mark S. Ashton;Jacob Abernethy;Fatemeh Afghah;Matthew H. E. M Browning;David Coyle;Philip Dames;Tom O'Halloran;James Hays;Patrick Heisl;Chenfanfu Jiang;Puskar Khanal;Venkat Narayan Krovi;Sara Kuebbing;Nianyi Li;JingJing Liang;Ninghao Liu;Steve McNulty;Christopher M. Oswalt;Neil Pederson;Demetri Terzopoulos;Christopher W. Woodall;Yongkai Wu;Jian Yang;Yin Yang;Liang Zhao
2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests r...Show More
Artificial Intelligence for Climate Smart Forestry: A Forward Looking Vision
Feng Luo;Ling Liu;G. Geoff Wang;Vijay Kumar;Mark S. Ashton;Jacob Abernethy;Fatemeh Afghah;Matthew H. E. M Browning;David Coyle;Philip Dames;Tom O'Halloran;James Hays;Patrick Heisl;Chenfanfu Jiang;Puskar Khanal;Venkat Narayan Krovi;Sara Kuebbing;Nianyi Li;JingJing Liang;Ninghao Liu;Steve McNulty;Christopher M. Oswalt;Neil Pederson;Demetri Terzopoulos;Christopher W. Woodall;Yongkai Wu;Jian Yang;Yin Yang;Liang Zhao
Year: 2023 | Conference Paper |
mBEST: Realtime Deformable Linear Object Detection Through Minimal Bending Energy Skeleton Pixel Traversals
Andrew Choi;Dezhong Tong;Brian Park;Demetri Terzopoulos;Jungseock Joo;Mohammad Khalid Jawed
Robotic manipulation of deformable materials is a challenging task that often requires realtime visual feedback. This is especially true for deformable linear objects (DLOs) or “rods”, whose slender and flexible structures make proper tracking and detection nontrivial. To address this challenge, we present mBEST, a robust algorithm for the realtime detection of DLOs that is capable of producing an...Show More
CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI
Alex Ling Yu Hung;Haoxin Zheng;Qi Miao;Steven S. Raman;Demetri Terzopoulos;Kyunghyun Sung
Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to se...Show More
RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging
Ali Hatamizadeh;Hamid Hosseini;Niraj Patel;Jinseo Choi;Cameron C. Pole;Cory M. Hoeferlin;Steven D. Schwartz;Demetri Terzopoulos
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical site where the microvasculature can be directly observed. The objective assessment of retinal vessels has long been considered a surrogate biomarker for...Show More
Image Segmentation Using Deep Learning: A Survey
Shervin Minaee;Yuri Boykov;Fatih Porikli;Antonio Plaza;Nasser Kehtarnavaz;Demetri Terzopoulos
Image segmentation is a key task in computer vision and image processing with important applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among others, and numerous segmentation algorithms are found in the literature. Against this backdrop, the broad success of deep learning (DL) has prompted the deve...Show More
Multimix: Sparingly-Supervised, Extreme Multitask Learning from Medical Images
Ayaan Haque;Abdullah-Al-Zubaer Imran;Adam Wang;Demetri Terzopoulos
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Semi-supervised learning from limited quantities of labeled data, an alternative to fully-supervised schemes, benefits by maximizing knowledge gains from copious unlabeled data. Furthermore, learning multiple tasks within the same model improves model generalizability. We propose MultiMix, a novel multitask learning model that jointly learns disease classification and anatomical segmentation in a ...Show More
A Location-Sensitive Local Prototype Network For Few-Shot Medical Image Segmentation
Qinji Yu;Kang Dang;Nima Tajbakhsh;Demetri Terzopoulos;Xiaowei Ding
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Year: 2021 | Conference Paper |
Cited by: Papers (31)
Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data. Few-shot segmentation approaches address this issue by learning to transfer knowledge from limited quantities of labeled examples. Incorporating appropriate prior knowledge is critical in designing high-performance few-shot segmentation...Show More
Progressive Adversarial Semantic Segmentation
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Medical image computing has advanced rapidly with the advent of deep learning techniques. Deep convolutional neural networks can perform well given full supervision. However, the success of such fully-supervised models in various image analysis tasks (e.g., anatomy or lesion segmentation from medical images) depends on the availability of massive quantities of labeled data. Given small sample size...Show More
Locally-Connected, Irregular Deep Neural Networks for Biomimetic Active Vision in a Simulated Human
Masaki Nakada;Honglin Chen;Arjun Lakshmipathy;Demetri Terzopoulos
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
An advanced simulation framework has recently been introduced for exploring human perception and visuomotor control. In this context, we investigate locally-connected, irregular deep neural networks (liNets) for biomimetic active vision. Like commonly used CNNs, liNets are locally-connected, forming receptive fields, but unlike CNNs, they are suitable for spatially irregular photoreceptor distribu...Show More
A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation
2020 25th International Conference on Pattern Recognition (ICPR)
Year: 2021 | Conference Paper |
Cited by: Papers (20)
Imaging anisotropy poses a critical challenge in applying deep learning models to 3D medical image analysis. Anisotropy downgrades model performance, especially when slice spacing varies significantly between training and clinical datasets. We propose a transformer-based model to tackle the anisotropy problem. It is adaptable to different levels of anisotropy and is computationally efficient. Our ...Show More
Partly Supervised Multi-Task Learning
Abdullah-Al-Zubaer Imran;Chao Huang;Hui Tang;Wei Fan;Yuan Xiao;Dingjun Hao;Zhen Qian;Demetri Terzopoulos
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model for multiple tasks can provide better generalizability than single-task learning. Leveraging self-supervision and adversarial training, we propose a novel, general purpose semi-supervised, multiple-task model-namely, sel...Show More
Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images
Abdullah-Al-Zubaer Imran;Chao Huang;Hui Tang;Wei Fan;Kenneth Cheung;Michael To;Zhen Qian;Demetri Terzopoulos
2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
Year: 2020 | Conference Paper |
Cited by: Papers (11)
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated me...Show More
Deep Learning of Neuromuscular and Visuomotor Control of a Biomimetic Simulated Humanoid
Masaki Nakada;Tao Zhou;Honglin Chen;Arjun Lakshmipathy;Demetri Terzopoulos
We present a biomimetic framework for human neuromuscular and visuomotor control that promises to be of value to researchers developing humanoid robots. Our framework features a biomechanically simulated human musculoskeletal model, actuated by numerous skeletal muscles, with realistic eyes driven by extraocular and intraocular muscles, whose optic organs refract light, and whose retinas have many...Show More
Door and Doorway Etiquette for Virtual Humans
We introduce a framework for simulating a variety of nontrivial, socially motivated behaviors that underlie the orderly passage of pedestrians through doorways, especially the common courtesy of opening and holding doors open for others, an important etiquette that has been overlooked in the literature on autonomous multi-human animation. Emulating such social activity requires serious attention t...Show More
Multi-adversarial Variational Autoencoder Networks
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform unsupervised clustering or semi-supervised classification of images. Combining the power of these two generative models, we introduce a novel network architecture, Multi-Adversarial Variational autoEncoder Networks (MAVENs), which incorporate an ensemble of discri...Show More
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
Nima Tajbakhsh;Yufei Hu;Junli Cao;Xingjian Yan;Yi Xiao;Yong Lu;Jianming Liang;Demetri Terzopoulos;Xiaowei Ding
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Year: 2019 | Conference Paper |
Cited by: Papers (44)
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underlying idea is to assign artificial labels to abundantly available unlabeled medical images and, through a process known as surrogate supervision, pre-train a deep neural network model for the target medical image analysis ...Show More
Fast and Scalable Position-Based Layout Synthesis
Tomer Weiss;Alan Litteneker;Noah Duncan;Masaki Nakada;Chenfanfu Jiang;Lap-Fai Yu;Demetri Terzopoulos
The arrangement of objects into a layout can be challenging for non-experts, as is affirmed by the existence of interior design professionals. Recent research into the automation of this task has yielded methods that can synthesize layouts of objects respecting aesthetic and functional constraints that are non-linear and competing. These methods usually adopt a stochastic optimization scheme, whic...Show More
Learning Biomimetic Perception for Human Sensorimotor Control
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
We introduce a biomimetic simulation framework for human perception and sensorimotor control. Our framework features a biomechanically simulated musculoskeletal human model actuated by numerous skeletal muscles, with two human-like eyes whose retinas contain spatially nonuniform distributions of photoreceptors. Its prototype sensorimotor system comprises a set of 20 automatically-trained deep neur...Show More
AcFR: Active Face Recognition Using Convolutional Neural Networks
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2017 | Conference Paper |
Cited by: Papers (23)
We propose AcFR, an active face recognition system that employs a convolutional neural network and acts consistently with human behaviors in common face recognition scenarios. AcFR comprises two main components-a recognition module and a controller module. The recognition module uses a pre-trained VGG-Face net to extract facial image features along with a nearest neighbor identity recognition algo...Show More
Attention-Based Natural Language Person Retrieval
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2017 | Conference Paper |
Cited by: Papers (6)
Following the recent progress in image classification and captioning using deep learning, we develop a novel natural language person retrieval system based on an attention mechanism. More specifically, given the description of a person, the goal is to localize the person in an image. To this end, we first construct a benchmark dataset for natural language person retrieval. To do so, we generate bo...Show More
Automated Layout Synthesis and Visualization from Images of Interior or Exterior Spaces
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2017 | Conference Paper |
Recent work in computer graphics has explored the synthesis of indoor spaces with furniture, accessories, and other layout items. In this work, we bridge the gap between the physical and virtual worlds: Given an input image of an interior or exterior space, and a general user specification of the desired furnishings and layout constraints, our method automatically furnishes the scene with a realis...Show More
Inferring Forces and Learning Human Utilities from Videos
Yixin Zhu;Chenfanfu Jiang;Yibiao Zhao;Demetri Terzopoulos;Song-Chun Zhu
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016 | Conference Paper |
Cited by: Papers (53)
We propose a notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects, and introduce a learning framework that incorporates the concept of human utilities, which in our opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects. Rather than defining affordanc...Show More
The Clutterpalette: An Interactive Tool for Detailing Indoor Scenes
We introduce the Clutterpalette, an interactive tool for detailing indoor scenes with small-scale items. When the user points to a location in the scene, the Clutterpalette suggests detail items for that location. In order to present appropriate suggestions, the Clutterpalette is trained on a dataset of images of real-world scenes, annotated with support relations. Our experiments demonstrate that...Show More
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