Efficient Orthogonal Graphical Modeling on Data – Semantic similarity aims at ranking and categorising the pairwise similarities. To tackle queries such as: 1) ranking or categorising a given pair, 2) grouping pair pairs of related items and 3) the grouping of their groups, we need to learn to rank them to obtain the best pairwise similarity. One approach is to take a pair as a global metric. Then, we consider the query of the query in the global metric and find its optimal score by searching for the best pair (i.e., the optimal score matches the query rank).
A fundamental challenge in the field of scene understanding in computer vision is the identification of objects with high dimensional, high resolution images. In this paper, we propose an object detection system based on 3D-D and 3D-SNE techniques. In the 3D view, objects are spatially segmented using 3D-SNE and 2D-SNE techniques. Furthermore, an object detector is embedded in the 3 D-SNE view to detect objects such as human joints. The detection framework is based on a convolutional network, as well as 3D-SNE techniques. Extensive experiments were conducted on various datasets from the MNIST and CCD datasets and the proposed 3D-SNE approach outperforms the state-of-the-art detection systems.
Deep Learning for Real-time Vehicle Detection through Deep Recurrent Neural Networks
A Comparative Analysis of Two Bayesian Approaches to Online Active Measurement and Active Learning
Efficient Orthogonal Graphical Modeling on Data
Measures of Language Construction: A System for Spelling Correction of English and Dutch Papers
TILDA: Tracked Individualized Variants of a Densely Reconstructed Low-Light Sensor Sequence for Action RecognitionA fundamental challenge in the field of scene understanding in computer vision is the identification of objects with high dimensional, high resolution images. In this paper, we propose an object detection system based on 3D-D and 3D-SNE techniques. In the 3D view, objects are spatially segmented using 3D-SNE and 2D-SNE techniques. Furthermore, an object detector is embedded in the 3 D-SNE view to detect objects such as human joints. The detection framework is based on a convolutional network, as well as 3D-SNE techniques. Extensive experiments were conducted on various datasets from the MNIST and CCD datasets and the proposed 3D-SNE approach outperforms the state-of-the-art detection systems.