Search Results for author:
Found 19 papers, 4 papers with code
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
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 #1 on
Monocular Depth Estimation
on KITTI Eigen split
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
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)
4D Panoptic Segmentation as Invariant and Equivariant Field Prediction
In this paper, we develop rotation-equivariant neural networks for 4D panoptic segmentation.
PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
Generalizable 3D part segmentation is important but challenging in vision and robotics.
Volumetric Attention for 3D Medical Image Segmentation and Detection
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed.
Validating uncertainty in medical image translation
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.
Finding novelty with uncertainty
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.
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods.
Outlier Guided Optimization of Abdominal Segmentation
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).
Stochastic tissue window normalization of deep learning on computed tomography
We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts.
Contrast Phase Classification with a Generative Adversarial Network
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.
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
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.
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
In this paper, we proposed two strategies to fuse information extracted from different modalities, i. e., audio and visual.
Identity-Free Facial Expression Recognition using conditional Generative Adversarial Network
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
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed.
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
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.
Improving Speech Related Facial Action Unit Recognition by Audiovisual Information Fusion
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.
Listen to Your Face: Inferring Facial Action Units from Audio Channel
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.
Facial Expression Recognition via a Boosted Deep Belief Network
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
