Learning from simulated and unsupervised images through adversarial training
With recent progress in graphics, it has become more tractable to train models on synthetic
images, potentially avoiding the need for expensive annotations. However, learning from …
images, potentially avoiding the need for expensive annotations. However, learning from …
Cut, paste and learn: Surprisingly easy synthesis for instance detection
A major impediment in rapidly deploying object detection models for instance detection is
the lack of large annotated datasets. For example, finding a large labeled dataset containing …
the lack of large annotated datasets. For example, finding a large labeled dataset containing …
Spatial fusion gan for image synthesis
Recent advances in generative adversarial networks (GANs) have shown great potentials in
realistic image synthesis whereas most existing works address synthesis realism in either …
realistic image synthesis whereas most existing works address synthesis realism in either …
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Learning to classify new categories based on just one or a few examples is a long-standing
challenge in modern computer vision. In this work, we propose a simple yet effective method …
challenge in modern computer vision. In this work, we propose a simple yet effective method …
Sparseness meets deepness: 3d human pose estimation from monocular video
This paper addresses the challenge of 3D full-body human pose estimation from a
monocular image sequence. Here, two cases are considered:(i) the image locations of the …
monocular image sequence. Here, two cases are considered:(i) the image locations of the …
Multi-level semantic feature augmentation for one-shot learning
The ability to quickly recognize and learn new visual concepts from limited samples enable
humans to quickly adapt to new tasks and environments. This ability is enabled by the …
humans to quickly adapt to new tasks and environments. This ability is enabled by the …
Learning to learn: Model regression networks for easy small sample learning
We develop a conceptually simple but powerful approach that can learn novel categories
from few annotated examples. In this approach, the experience with already learned …
from few annotated examples. In this approach, the experience with already learned …
Synthesizing training images for boosting human 3d pose estimation
Human 3D pose estimation from a single image is a challenging task with numerous
applications. Convolutional Neural Networks (CNNs) have recently achieved superior …
applications. Convolutional Neural Networks (CNNs) have recently achieved superior …
Mocap-guided data augmentation for 3d pose estimation in the wild
This paper addresses the problem of 3D human pose estimation in the wild. A significant
challenge is the lack of training data, ie, 2D images of humans annotated with 3D poses …
challenge is the lack of training data, ie, 2D images of humans annotated with 3D poses …
Monocap: Monocular human motion capture using a cnn coupled with a geometric prior
Recovering 3D full-body human pose is a challenging problem with many applications. It
has been successfully addressed by motion capture systems with body worn markers and …
has been successfully addressed by motion capture systems with body worn markers and …