Super-Dense: Robust Deep Convolutional Neural Network Embedding via Self-Adaptive Regularization – We investigate supervised deep learning for visual tracking. We propose a technique that extracts a representation of the sensor-dependent motion of the object and a neural network that uses a convolutional neural network to predict the appearance and orientation of the object accordingly. This representation can be used by using a convolutional neural network based on object-view-label pairs. We design and test a deep tracking system to accurately track a pair of objects. Through experimental evaluation, we demonstrate the effectiveness of our approach and demonstrate the effectiveness of our system on various real-world datasets.
The first part of this paper describes our first work on the problem of automatically inferring human identities in the form of a graphical representation of their appearances. Although these algorithms are useful in many situations, in order to understand their performance and predict future progress we need a large amount of data. We also propose two novel datasets to test these algorithms for their effectiveness and performance. Using the ImageNet benchmark dataset we can find that the proposed methods significantly outperform baseline saliency prediction tasks without significant changes in the state of the art. The key insight we make is that in general the network performs better than saliency prediction in both the high contrast and low contrast settings. In addition, the main benefit is that saliency predictions with more contrast are more likely to be accurate in both the high contrast and low contrast scenarios.
Learning from Humans: Deep Face Recognition for Early Visual History and Motion Recognition
3D Scanning Network for Segmentation of Medical Images
Super-Dense: Robust Deep Convolutional Neural Network Embedding via Self-Adaptive Regularization
Universal Dependency-Aware Knowledge Base Completion
A Study of Two Problems in Visual Saliency Classification: An Interactive Scenario and Three-Dimensional ScenarioThe first part of this paper describes our first work on the problem of automatically inferring human identities in the form of a graphical representation of their appearances. Although these algorithms are useful in many situations, in order to understand their performance and predict future progress we need a large amount of data. We also propose two novel datasets to test these algorithms for their effectiveness and performance. Using the ImageNet benchmark dataset we can find that the proposed methods significantly outperform baseline saliency prediction tasks without significant changes in the state of the art. The key insight we make is that in general the network performs better than saliency prediction in both the high contrast and low contrast settings. In addition, the main benefit is that saliency predictions with more contrast are more likely to be accurate in both the high contrast and low contrast scenarios.