Clustering-Based Feature Representations for Person Re-Identification – Deep object detectors often provide a large amount of useful contextual information in order to improve classification accuracy. However, they are also prone to overfitting. Recent work on deep-dense recurrent neural network (CNN) has achieved a significant improvement in classification accuracy. However, CNNs can suffer from overfitting at a crucial level. In order to address this issue, we propose a novel framework to learn features from deep CNNs. Specifically, CNNs learn models for object categories, i.e. those with multiple classifiers. They are capable of incorporating contextual information and making predictions from the CNN’s features, so that the classification accuracy of the CNN is directly dependent on the semantic information provided by the object categories. In our work, we propose a simple and efficient learning procedure to learn object categories, in order to improve classification accuracy on CNNs trained on object categories. The proposed approach shows promising performance compared to other deep CNNs and a state-of-the-art CNN. We will evaluate this approach, and illustrate it in various applications.

We present an active learning strategy for object segmentation using the recently built Convolutional Recurrent Neural Network (Recurrent-RNN), which can be easily adapted to any task. As a result, it can learn and predict object poses from unseen data. To our knowledge, no activity recognition task has been directly applied to a supervised object segmentation task for which the object position is the only important information. We propose a novel CNN-based active recognition method to segment the object, and apply it to a large-scale, multi-object action recognition task. This method is able to learn representations of the object pose and pose and predict the number of events for each individual event, and we propose an algorithm that learns the pose and pose in an end-to-end manner. We show that our method achieves state-of-the-art performance in the ROC task of object segmentation, and that it also outperforms the existing state-of-the-art object segmentation methods.

On-the-fly LSTM for Action Recognition on Video via a Spatio-Temporal Algorithm

End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points

Clustering-Based Feature Representations for Person Re-Identification

Learning A Comprehensive Classifier

On-line learning of spatiotemporal patterns using an exact node-distance approachWe present an active learning strategy for object segmentation using the recently built Convolutional Recurrent Neural Network (Recurrent-RNN), which can be easily adapted to any task. As a result, it can learn and predict object poses from unseen data. To our knowledge, no activity recognition task has been directly applied to a supervised object segmentation task for which the object position is the only important information. We propose a novel CNN-based active recognition method to segment the object, and apply it to a large-scale, multi-object action recognition task. This method is able to learn representations of the object pose and pose and predict the number of events for each individual event, and we propose an algorithm that learns the pose and pose in an end-to-end manner. We show that our method achieves state-of-the-art performance in the ROC task of object segmentation, and that it also outperforms the existing state-of-the-art object segmentation methods.