Deep neural network training with hidden panels for nonlinear adaptive filtering – We present a novel network-model-guided approach to learning to-watch video data. Through a deep learning method that learns an encoding function for each frame of the video sequence, the network is trained with an eye-tracking strategy on the sequence, which is then used to predict future frames of the relevant sequence. Our model uses a multi-sensor convolutional neural network that can learn the visual attribute of the input video. We propose a novel framework, called ConvNet-CNN, to learn the visual attribute of the input video from multi-view regression. We show that our method outperforms three state-of-the-art CNN architectures on various datasets.
As the Web continues to evolve and evolve in unprecedented ways, and as people consume and interact with images and videos each day, the Web has become a powerful tool for the analysis of social interactions. We aim and conduct a real-time visual search for a common visual pattern of images and videos, and perform this search with a knowledge of what information in these images and videos are shared with each other using the web. We compare some approaches and show that visual search can be used to find related visual patterns, and present preliminary results to evaluate visual search techniques such as visual similarity and similarity discovery.
Advances in Probabilistic Modeling of Knowledge
Deep neural network training with hidden panels for nonlinear adaptive filtering
Constrained Two-Stage Multiple Kernel Learning for Graph Signals
Learning to Find and Recommend Similarities Across Images and VideosAs the Web continues to evolve and evolve in unprecedented ways, and as people consume and interact with images and videos each day, the Web has become a powerful tool for the analysis of social interactions. We aim and conduct a real-time visual search for a common visual pattern of images and videos, and perform this search with a knowledge of what information in these images and videos are shared with each other using the web. We compare some approaches and show that visual search can be used to find related visual patterns, and present preliminary results to evaluate visual search techniques such as visual similarity and similarity discovery.