Deep Predictive Models for Visual Recognition – In this paper, we propose a new method for learning visual object segmentation using an online framework called Online-CNN, which is able to learn object class hierarchies from image features. Unlike object classification, object segmentation can be performed in an online manner. The method achieves state-of-the-art performance on public and private datasets of the COCO scene dataset, which also has an ongoing evaluation of our approach. In particular, the method was evaluated on the ImageNet ImageNet dataset, which contains 10K images in the COCO dataset. The method is a deep learning method trained locally on our COCO object dataset. Our method achieves state-of-the-art results on both the publicly dataset and online data.

This paper presents a novel method for extracting 3D shape from 3D video. The 3D shape is sampled from multiple views in 3D video, and the 3D shape is extracted using an embedding-based representation based on RGB-D sensors. The 3D shape is annotated using a deep convolutional neural network as the input and the 3D shape is extracted using a pre-trained recurrent neural network. The 3D shape is then segmented using a depth map of the 3D surface map, extracted using a recurrent neural network, and finally segmented using a convolutional neural network. Extensive evaluation in real 3D video sequences shows that our method significantly outperforms other state-of-the-art methods.

On the Relation between the Random Forest-based Random Forest and the Random Forest Model

A Comparison of Two Observational Wind Speed Estimation Techniques on Satellite Images

Deep Predictive Models for Visual Recognition

Video Anomaly Detection Using Learned Convnet Features

Towards Optimal Vehicle Detection and SteeringThis paper presents a novel method for extracting 3D shape from 3D video. The 3D shape is sampled from multiple views in 3D video, and the 3D shape is extracted using an embedding-based representation based on RGB-D sensors. The 3D shape is annotated using a deep convolutional neural network as the input and the 3D shape is extracted using a pre-trained recurrent neural network. The 3D shape is then segmented using a depth map of the 3D surface map, extracted using a recurrent neural network, and finally segmented using a convolutional neural network. Extensive evaluation in real 3D video sequences shows that our method significantly outperforms other state-of-the-art methods.