3.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nvidia (United Kingdom)GB136 papers
- Lawrence Berkeley National LaboratoryUS32 papers
- Stanford UniversityUS30 papers
- University of California, BerkeleyUS29 papers
- California Institute of TechnologyUS26 papers
- Massachusetts Institute of TechnologyUS25 papers
- Carnegie Mellon UniversityUS24 papers
- University of Illinois Urbana-ChampaignUS24 papers
- University of TorontoCA24 papers
- University of WashingtonUS23 papers
- Harvard UniversityUS19 papers
- Georgia Institute of TechnologyUS17 papers
24 papers · 2 filters
One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
Ting-Chun Wang, Arun Mallya, Ming-Yu Liu
We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing. Our model learns to synthesize a talking-head video using a source ima…
Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies
Haomin Chen, Shun Miao, Daguang Xu +2
CXRs are a crucial and extraordinarily common diagnostic tool, leading to heavy research for CAD solutions. However, both high classification accuracy and meaningful model predicti…
DeepGMR: Learning Latent Gaussian Mixture Models for Registration
Wentao Yuan, Ben Eckart, Kihwan Kim +3
Point cloud registration is a fundamental problem in 3D computer vision, graphics and robotics. For the last few decades, existing registration algorithms have struggled in situati…
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data
Meng Ye, Qiaoying Huang, Dong Yang +4
The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.…
Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies
Leo K. Tam, Xiaosong Wang, Evrim Turkbey +3
Detecting clinically relevant objects in medical images is a challenge despite large datasets due to the lack of detailed labels. To address the label issue, we utilize the scene-l…
Fully Dynamic Inference with Deep Neural Networks
Wenhan Xia, Hongxu Yin, Xiaoliang Dai +1
Modern deep neural networks are powerful and widely applicable models that extract task-relevant information through multi-level abstraction. Their cross-domain success, however, i…
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
Yang Zou, Xiaodong Yang, Zhiding Yu +2
Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge…
Multi-Domain Image Completion for Random Missing Input Data
Liyue Shen, Wentao Zhu, Xiaosong Wang +9
Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-par…
Auxiliary Learning by Implicit Differentiation
Aviv Navon, Idan Achituve, Haggai Maron +2
Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning s…
Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation
Yingda Xia, Dong Yang, Zhiding Yu +7
Although having achieved great success in medical image segmentation, deep learning-based approaches usually require large amounts of well-annotated data, which can be extremely ex…
LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation
Wentao Zhu, Can Zhao, Wenqi Li +3
Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data paralle…
A Closed-Form Uncertainty Propagation in Non-Rigid Structure from Motion
Jingwei Song, Mitesh Patel, Ashkan Jasour +1
Semi-Definite Programming (SDP) with low-rank prior has been widely applied in Non-Rigid Structure from Motion (NRSfM). Based on a low-rank constraint, it avoids the inherent ambig…
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera
Jae Shin Yoon, Kihwan Kim, Orazio Gallo +2
This paper presents a new method to synthesize an image from arbitrary views and times given a collection of images of a dynamic scene. A key challenge for the novel view synthesis…
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
Zhongzheng Ren, Zhiding Yu, Xiaodong Yang +4
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1)…
GANSpace: Discovering Interpretable GAN Controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen +1
This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging,…
Two-shot Spatially-varying BRDF and Shape Estimation
Mark Boss, Varun Jampani, Kihwan Kim +2
Capturing the shape and spatially-varying appearance (SVBRDF) of an object from images is a challenging task that has applications in both computer vision and graphics. Traditional…
Context Based Emotion Recognition using EMOTIC Dataset
Ronak Kosti, Jose M. Alvarez, Adria Recasens +1
In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacit…
Gaze-Sensing LEDs for Head Mounted Displays
Kaan Akşit, Jan Kautz, David Luebke
We introduce a new gaze tracker for Head Mounted Displays (HMDs). We modify two off-the-shelf HMDs to be gaze-aware using Light Emitting Diodes (LEDs). Our key contribution is to e…
Self-supervised Single-view 3D Reconstruction via Semantic Consistency
Xueting Li, Sifei Liu, Kihwan Kim +4
We learn a self-supervised, single-view 3D reconstruction model that predicts the 3D mesh shape, texture and camera pose of a target object with a collection of 2D images and silho…
Semi-Supervised StyleGAN for Disentanglement Learning
Weili Nie, Tero Karras, Animesh Garg +4
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: diffi…
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
Xiang Wang, Sifei Liu, Huimin Ma +1
Weakly-supervised semantic segmentation is a challenging task as no pixel-wise label information is provided for training. Recent methods have exploited classification networks to…
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat +66
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit cl…
Exploiting Semantics for Face Image Deblurring
Ziyi Shen, Wei-Sheng Lai, Tingfa Xu +2
In this paper, we propose an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks. As the human faces are highly str…
Meshlet Priors for 3D Mesh Reconstruction
Abhishek Badki, Orazio Gallo, Jan Kautz +1
Estimating a mesh from an unordered set of sparse, noisy 3D points is a challenging problem that requires carefully selected priors. Existing hand-crafted priors, such as smoothnes…