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Found 9 papers, 1 papers with code
Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies
Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation.
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location.
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
A Deep Learning Approach to Drone Monitoring
A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work.
Deep 3D Face Identification
We also propose a 3D face augmentation technique which synthesizes a number of different facial expressions from a single 3D face scan.
Pooling Faces: Template based Face Recognition with Pooled Face Images
We propose a novel approach to template based face recognition.
Face Recognition Using Deep Multi-Pose Representations
In our representation, a face image is processed by several pose-specific deep convolutional neural network (CNN) models to generate multiple pose-specific features.
Ranked #14 on
Face Verification
on IJB-A
3D Modeling from Wide Baseline Range Scans using Contour Coherence
We propose here a novel approach which leverages contour coherence and allows us to align two wide baseline range scans with limited overlap from a poor initialization.
