Reconstructing images of traffic video with word embeddings: a multi-dimensional framework – We present a general algorithm for identifying human gestures using word embeddings on image data. In particular, a word embeddings is an effective descriptor for recognizing gestures that are consistent with a given visual description. Our model is based on the notion of a semantic semantic similarity. The semantic similarity determines which regions correspond to the desired gestures. We show that a semantic-semantic similarity could be used to discriminate people with gestures. By contrast, our model is formulated as a feature extraction model. We further provide a simple computational model for the semantic-semantic similarity that we use to demonstrate the approach. Finally, we experiment the approach on the task of recognizing gestures using text descriptions of people.

This paper presents a deep adversarial learning technique (DLIST) for detecting unsupervised and supervised patterns in synthetic data consisting of unsupervised activity recognition patterns. By means of a multi-layer recurrent neural network (RNN) equipped with features that we learned a priori, we were able to reliably detect patterns that are similar to patterns from other tasks and that have similar distributional structure. Experiments show that our DLIST algorithm outperforms state- of-the-art approaches in several tasks, achieving a classification accuracy comparable to those of state-of-the-art supervised patterns, and using similar features, but with a better computational efficiency.

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Reconstructing images of traffic video with word embeddings: a multi-dimensional framework

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Sparse and Robust Gaussian Processes with Dynamic TSPsThis paper presents a deep adversarial learning technique (DLIST) for detecting unsupervised and supervised patterns in synthetic data consisting of unsupervised activity recognition patterns. By means of a multi-layer recurrent neural network (RNN) equipped with features that we learned a priori, we were able to reliably detect patterns that are similar to patterns from other tasks and that have similar distributional structure. Experiments show that our DLIST algorithm outperforms state- of-the-art approaches in several tasks, achieving a classification accuracy comparable to those of state-of-the-art supervised patterns, and using similar features, but with a better computational efficiency.