62 citations · 245 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…