62 citations · 245 across the 13 of their papers we have counts for
16 papers
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Minghua Liu, Ruoxi Shi, Kaiming Kuang +6
We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framew…
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Minghua Liu, Yinhao Zhu, Hong Cai +4
Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets wi…
Volumetric Attention for 3D Medical Image Segmentation and Detection
Xudong Wang, Shizhong Han, Yunqiang Chen +2
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D network…
Validating uncertainty in medical image translation
Jacob C. Reinhold, Yufan He, Shizhong Han +5
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do…
Finding novelty with uncertainty
Jacob C. Reinhold, Yufan He, Shizhong Han +5
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because…
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiologic…
Outlier Guided Optimization of Abdominal Segmentation
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…
Stochastic tissue window normalization of deep learning on computed tomography
Yuankai Huo, Yucheng Tang, Yunqiang Chen +8
Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal C…
Contrast Phase Classification with a Generative Adversarial Network
Yucheng Tang, Ho Hin Lee, Yuchen Xu +10
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
Ho Hin Lee, Yucheng Tang, Olivia Tang +9
Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and e…
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
Jie Cai, Zibo Meng, Ahmed Shehab Khan +6
Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved fo…
Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network
Jie Cai, Zibo Meng, Ahmed Shehab Khan +4
A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations ca…
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
Shizhong Han, Zibo Meng, Ahmed Shehab Khan +1
Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However,…
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Shizhong Han, Zibo Meng, Zhiyuan Li +4
Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial…
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
Zibo Meng, Shizhong Han, Ping Liu +1
It is challenging to recognize facial action unit (AU) from spontaneous facial displays, especially when they are accompanied by speech. The major reason is that the information is…
Listen to Your Face: Inferring Facial Action Units from Audio Channel
Zibo Meng, Shizhong Han, Yan Tong
Extensive efforts have been devoted to recognizing facial action units (AUs). However, it is still challenging to recognize AUs from spontaneous facial displays especially when the…