Deep Learning Models for Multi-Modal Human Action Recognition


Deep Learning Models for Multi-Modal Human Action Recognition – This paper describes the application of the deep learning method for social interaction detection to the Human-Object Context of an object, by solving the challenging task of object and context prediction. As this is the first attempt, which consists in solving two related problems: the first one is the problem of learning a semantic-semantic model for the object and the second one is the problems of learning a semantic-semantic model for the context. The two related problems are (1) learning semantic models for objects, and (2) learning a semantic model for the context. We evaluate our algorithm on two real world datasets, and show that the semantic-semantic model outperforms baselines on both tasks. Finally, we present our method for the recognition of objects in the wild.

We propose a novel approach to the optimization of the segmentation task of a large-scale dataset. As part of our research, we propose a novel method of estimating the shape of the segmentation task. This involves learning a neural model to predict the shape of a segmented image by applying different segmentation approaches according to an input model and a visual cue detector. The learned model can predict the shape accurately by learning a similarity metric and modeling the segmentation task as a multi-view sequential data. We further propose a novel multi-view sequence labeling framework. The proposed method is learned from different models on multiple datasets with different segmentation and visual cues detectors. The results of the evaluation of the proposed approach show that its performance compares well to the state-of-the-art methods while it is less accurate. Furthermore, we observed that our approach outperforms a deep framework in terms of classification accuracy.

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Deep Learning Models for Multi-Modal Human Action Recognition

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  • Towards CNN-based Image Retrieval with Multi-View Fusion

    Toward Scalable Graph Convolutional Neural Network Clustering for Multi-Label Health PredictorsWe propose a novel approach to the optimization of the segmentation task of a large-scale dataset. As part of our research, we propose a novel method of estimating the shape of the segmentation task. This involves learning a neural model to predict the shape of a segmented image by applying different segmentation approaches according to an input model and a visual cue detector. The learned model can predict the shape accurately by learning a similarity metric and modeling the segmentation task as a multi-view sequential data. We further propose a novel multi-view sequence labeling framework. The proposed method is learned from different models on multiple datasets with different segmentation and visual cues detectors. The results of the evaluation of the proposed approach show that its performance compares well to the state-of-the-art methods while it is less accurate. Furthermore, we observed that our approach outperforms a deep framework in terms of classification accuracy.


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