no code implementations • 19 Mar 2024 • Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #2 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • NeurIPS 2023 • Minghua Liu, Ruoxi Shi, Kaiming Kuang, Yinhao Zhu, Xuanlin Li, Shizhong Han, Hong Cai, Fatih Porikli, Hao Su
Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.
Ranked #5 on Zero-Shot Transfer 3D Point Cloud Classification on ModelNet40 (using extra training data)
no code implementations • ICCV 2023 • Minghan Zhu, Shizhong Han, Hong Cai, Shubhankar Borse, Maani Ghaffari, Fatih Porikli
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation.
2 code implementations • CVPR 2023 • Minghua Liu, Yinhao Zhu, Hong Cai, Shizhong Han, Zhan Ling, Fatih Porikli, Hao Su
Generalizable 3D part segmentation is important but challenging in vision and robotics.
no code implementations • 4 Apr 2020 • Xudong Wang, Shizhong Han, Yunqiang Chen, Dashan Gao, Nuno Vasconcelos
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed.
2 code implementations • 11 Feb 2020 • Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely.
1 code implementation • 11 Feb 2020 • Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.
no code implementations • 10 Feb 2020 • Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).
no code implementations • 10 Feb 2020 • Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods.
no code implementations • 1 Dec 2019 • Yuankai Huo, Yucheng Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Shunxing Bao, Smita De, James G. Terry, Jeffrey J. Carr, Richard G. Abramson, Bennett A. Landman
We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts.
no code implementations • 14 Nov 2019 • Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
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 of underlying anatomy.
no code implementations • 12 Nov 2019 • Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.
no code implementations • 6 Jun 2019 • Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Shizhong Han, Ping Liu, Min Chen, Yan Tong
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
no code implementations • 19 Mar 2019 • Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Shizhong Han, Yan Tong
A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations caused by identity-related facial attributes, e. g., age, race, and gender.
Facial Expression Recognition Facial Expression Recognition (FER) +1
no code implementations • CVPR 2018 • Shizhong Han, Zibo Meng, Zhiyuan Li, James O'Reilly, Jie Cai, Xiao-Feng Wang, Yan Tong
Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed.
no code implementations • NeurIPS 2016 • Shizhong Han, Zibo Meng, Ahmed Shehab Khan, Yan Tong
Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition.
no code implementations • 29 Jun 2017 • Zibo Meng, Shizhong Han, Ping Liu, Yan Tong
Instead of solely improving visual observations, this paper presents a novel audiovisual fusion framework, which makes the best use of visual and acoustic cues in recognizing speech-related facial AUs.
no code implementations • 23 Jun 2017 • Zibo Meng, Shizhong Han, Yan Tong
Different from all prior work that utilized visual observations for facial AU recognition, this paper presents a novel approach that recognizes speech-related AUs exclusively from audio signals based on the fact that facial activities are highly correlated with voice during speech.
no code implementations • CVPR 2014 • Ping Liu, Shizhong Han, Zibo Meng, Yan Tong
A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction.
Facial Expression Recognition Facial Expression Recognition (FER) +1