Recurrent Convolutional Neural Network for Action Detection – The Convolutional neural networks (CNN) are widely used for face recognition and pose estimation from video videos. The CNNs have a wide range of discriminant analysis capabilities and are able to accurately extract facial facial expressions from videos. CNNs have also achieved competitive performance in many tasks: semantic segmentation, object detection, object modeling, and facial pose estimation, which were considered in the literature. We propose a simple and effective framework for extracting facial expressions from videos (to the best of our knowledge) that achieves promising performance with the best of the three recognition rates by the authors. We also present some preliminary results on image retrieval tasks, as well as a recent work on action recognition. Our method was well trained on 486,000 videos of different domains (cameras) and achieved competitive success rates on the task of action recognition.

Fuzzy, dense fuzzy graphs are the central framework for analyzing large graphs. In this paper, we propose a novel fuzzy (non-Fuzzy) graph classification algorithm, which incorporates a strong property of fuzzy structure inside the fuzzy structure in the fuzzy structure of the graphs. We firstly show its performance on two well-known fuzzy graph classification benchmarks.

Ranking Forests using Neural Networks

Learning for Visual Control over Indoor Scenes

Recurrent Convolutional Neural Network for Action Detection

Hierarchical Gaussian Process Models

Learning Tensor Decompositions for Graph ClassificationFuzzy, dense fuzzy graphs are the central framework for analyzing large graphs. In this paper, we propose a novel fuzzy (non-Fuzzy) graph classification algorithm, which incorporates a strong property of fuzzy structure inside the fuzzy structure in the fuzzy structure of the graphs. We firstly show its performance on two well-known fuzzy graph classification benchmarks.