84 citations · 272 across the 15 of their papers we have counts for
24 papers
MindAgent: Emergent Gaming Interaction
Ran Gong, Qiuyuan Huang, Xiaojian Ma +8
Large Language Models (LLMs) have the capacity of performing complex scheduling in a multi-agent system and can coordinate these agents into completing sophisticated tasks that req…
mBEST: Realtime Deformable Linear Object Detection Through Minimal Bending Energy Skeleton Pixel Traversals
Andrew Choi, Dezhong Tong, Brian Park +3
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 "ro…
Learning Neural Force Manifolds for Sim2Real Robotic Symmetrical Paper Folding
Andrew Choi, Dezhong Tong, Demetri Terzopoulos +2
Robotic manipulation of slender objects is challenging, especially when the induced deformations are large and nonlinear. Traditionally, learning-based control approaches, such as…
Neuromuscular Control of the Face-Head-Neck Biomechanical Complex With Learning-Based Expression Transfer From Images and Videos
Xiao S. Zeng, Surya Dwarakanath, Wuyue Lu +2
The transfer of facial expressions from people to 3D face models is a classic computer graphics problem. In this paper, we present a novel, learning-based approach to transferring…
A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation
Qinji Yu, Kang Dang, Nima Tajbakhsh +2
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 segment…
MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images
Ayaan Haque, Abdullah-Al-Zubaer Imran, Adam Wang +1
Semi-supervised learning via learning from limited quantities of labeled data has been investigated as an alternative to supervised counterparts. Maximizing knowledge gains from co…
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery
Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos
The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large i…
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang +5
Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conv…
Progressive Adversarial Semantic Segmentation
Abdullah-Al-Zubaer Imran, Demetri Terzopoulos
Medical image computing has advanced rapidly with the advent of deep learning techniques such as convolutional neural networks. Deep convolutional neural networks can perform excee…
Edge-Gated CNNs for Volumetric Semantic Segmentation of Medical Images
Ali Hatamizadeh, Demetri Terzopoulos, Andriy Myronenko
Textures and edges contribute different information to image recognition. Edges and boundaries encode shape information, while textures manifest the appearance of regions. Despite…
Image Segmentation Using Deep Learning: A Survey
Shervin Minaee, Yuri Boykov, Fatih Porikli +3
Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveilla…
End-to-End Deep Convolutional Active Contours for Image Segmentation
Ali Hatamizadeh, Debleena Sengupta, Demetri Terzopoulos
The Active Contour Model (ACM) is a standard image analysis technique whose numerous variants have attracted an enormous amount of research attention across multiple fields. Incorr…
End-to-End Boundary Aware Networks for Medical Image Segmentation
Ali Hatamizadeh, Demetri Terzopoulos, Andriy Myronenko
Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class mas…
Semi-Supervised Multi-Task Learning With Chest X-Ray Images
Abdullah-Al-Zubaer Imran, Demetri Terzopoulos
Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable. By contrast, generative modeling---…
Deep Active Lesion Segmentation
Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta +4
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable…
Multi-Adversarial Variational Autoencoder Networks
Abdullah-Al-Zubaer Imran, Demetri Terzopoulos
The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform image-based unsupervised clustering or semi-…
Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
Ali Hatamizadeh, Hamid Hosseini, Zhengyuan Liu +2
The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, includin…
Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
Abdullah-Al-Zubaer Imran, Ali Hatamizadeh, Shilpa P. Ananth +3
Reliable and automatic segmentation of lung lobes is important for diagnosis, assessment, and quantification of pulmonary diseases. The existing techniques are prohibitively slow,…
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
Nima Tajbakhsh, Yufei Hu, Junli Cao +6
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 underly…
Learning to Sketch with Deep Q Networks and Demonstrated Strokes
Tao Zhou, Chen Fang, Zhaowen Wang +5
Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simula…
Fast and Scalable Position-Based Layout Synthesis
Tomer Weiss, Alan Litteneker, Noah Duncan +4
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 o…
Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)
Tomer Weiss, Alan Litteneker, Chenfanfu Jiang +1
Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of a…
Attention-based Natural Language Person Retrieval
Tao Zhou, Muhao Chen, Jie Yu +1
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 mechanis…
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars
Chenfanfu Jiang, Siyuan Qi, Yixin Zhu +5
We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with assoc…