A Large Scale Benchmark Dataset for Multimedia Video Annotation and Creation Evaluation – Recent work in video parsing has shown that extracting features from a video is a major challenge due to an unrealistic representation. In this work we propose a novel approach that allows for a natural and accurate representation of video content. We define a graph representation of video content over a set of attributes and a video parsing algorithm over the graph is used to extract features. Our model considers the representation of video content in a semantic space. Using this representation, we improve the parsing performance by a factor of 3 to a factor of 6 in terms of the number of semantic features extracted. The proposed approach is the first large scale video parsing algorithm for videos and a recent extension has been proposed to a novel and high quality parsing method. We present a video parsing algorithm that significantly outperforms state-of-the-art video parsing and video parsing algorithms in terms of the semantic representation.
Computational models are well suited for semantic image reconstruction which has been a key challenge in recent years. The current deep learning based method is primarily based on applying convolutional neural networks on top of a regularizer like Generative Adversarial Network (GAN). By combining the regularizer with a deep learning model, one can achieve high compression quality and fast image retrieval. In this work, the proposed method is compared to two other popular deep learning based models: SVM and CNN. The performance of the proposed method is shown to be comparable to the state-of-the-art Deep Learning based method in terms of the recognition, retrieval and retrieval speed. The proposed method achieves the best retrieval result of 0.914% vs. 0.08$% for both CNN and SVM. The proposed method also achieved an accuracy of 0.113% and an accuracy of 0.113% for CNN and 0.113% for SVM.
Fast, Accurate Metric Learning
A Novel Feature Selection Method Using Backpropagation for Propositional Formula Matching
A Large Scale Benchmark Dataset for Multimedia Video Annotation and Creation Evaluation
A survey of perceptual-motor training
Deep Learning for Compression Artifacts DetectionComputational models are well suited for semantic image reconstruction which has been a key challenge in recent years. The current deep learning based method is primarily based on applying convolutional neural networks on top of a regularizer like Generative Adversarial Network (GAN). By combining the regularizer with a deep learning model, one can achieve high compression quality and fast image retrieval. In this work, the proposed method is compared to two other popular deep learning based models: SVM and CNN. The performance of the proposed method is shown to be comparable to the state-of-the-art Deep Learning based method in terms of the recognition, retrieval and retrieval speed. The proposed method achieves the best retrieval result of 0.914% vs. 0.08$% for both CNN and SVM. The proposed method also achieved an accuracy of 0.113% and an accuracy of 0.113% for CNN and 0.113% for SVM.