SQNet: Predicting the expected behavior of a target system using neural network – We propose a simple, scalable neural network for action prediction (ASP) tasks. The proposed algorithm is efficient despite the fact that the proposed algorithm does not require a pre-trained neural network model and can be trained from scratch. In addition, it is robust to misprediction. In this paper, we present the results of our study of the performance of a neural network for a single task. We show that the proposed neural network can be used to predict the expected behavior of a new task from the input data produced by the new task (i.e., learning a new task).
This study proposes a new technique for 3D reconstruction from partial deformation measurements in low- and high-resolution datasets. This is accomplished by constructing the partial measurements for each point in a deformable space, based on a mapping scheme of a data-rich optical flow. This mapping scheme is also exploited by extracting the high-resolution reconstruction from data. To tackle the problem of high-resolution deformation measurements, the proposed technique is first applied to a large dataset of deformable signals, and then combines the reconstructed partial measurements to improve the reconstruction performance. Experiments on simulated and real deformation measurements indicate that the proposed approach achieves comparable results compared to state-of-the-art methods.
Evolving Feature-Based Object Localization with ConvNets
SQNet: Predicting the expected behavior of a target system using neural network
Generating More Reliable Embeddings via Semantic Parsing
Robust Depth Map Estimation Using Motion Vector RepresentationsThis study proposes a new technique for 3D reconstruction from partial deformation measurements in low- and high-resolution datasets. This is accomplished by constructing the partial measurements for each point in a deformable space, based on a mapping scheme of a data-rich optical flow. This mapping scheme is also exploited by extracting the high-resolution reconstruction from data. To tackle the problem of high-resolution deformation measurements, the proposed technique is first applied to a large dataset of deformable signals, and then combines the reconstructed partial measurements to improve the reconstruction performance. Experiments on simulated and real deformation measurements indicate that the proposed approach achieves comparable results compared to state-of-the-art methods.
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