Spatially-Sparse Convolution Neural Networks for Mobile Vision

Spatially-Sparse Convolution Neural Networks for Mobile Vision – We propose a two-stage pipeline for deep neural network (DNN) classification: (1) a training on image frames and (2) a testing on a visual image. Our proposed approach is simple: it takes input frames per image from the training dataset and trains a deep neural network (DNN) to classify the frame pairs of different frames. As the representation is generated from raw frames, CNN and Convolutional Neural Network learn to classify them. The training time is reduced by using the CNN-to-DNN approach, and the CNN-to-DNN approach is used for learning the convolutional neural network representations. We evaluate the proposed model on various challenging vision tasks including object identification from video, face recognition from visual field (e.g., hand gesture recognition), recognition of object parts in a car, face detection from a mobile robot, motion based face recognition and human pose estimation. We achieve state-of-the-art performance on the challenging CelebA dataset.

This paper presents a novel framework for automatically recognizing faces in video content from social media content. We extend previous work on social media face recognition in terms of face classification and face classification in videos. This new framework allows to automatically recognize face images for social media content and can identify faces for video content using an improved Convolutional Neural Network (CNN). We show that face classification in video content is much more challenging than face categories. To cope with this difficulty we propose a new deep learning-based framework called ‘Face Recognition in Video’ (FREV) using a deep Convolutional Neural Network (CNN) which utilizes the multi-view convolutional network to directly model faces for video content. Our framework improves state-of-the-art face recognition performance over the deep network in terms of face classification of videos for FBV and videos for PNN.

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Spatially-Sparse Convolution Neural Networks for Mobile Vision

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    Pseudoregressive Neural Network (PNN) for Image ClassificationThis paper presents a novel framework for automatically recognizing faces in video content from social media content. We extend previous work on social media face recognition in terms of face classification and face classification in videos. This new framework allows to automatically recognize face images for social media content and can identify faces for video content using an improved Convolutional Neural Network (CNN). We show that face classification in video content is much more challenging than face categories. To cope with this difficulty we propose a new deep learning-based framework called ‘Face Recognition in Video’ (FREV) using a deep Convolutional Neural Network (CNN) which utilizes the multi-view convolutional network to directly model faces for video content. Our framework improves state-of-the-art face recognition performance over the deep network in terms of face classification of videos for FBV and videos for PNN.


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