DeepFace 2: Face Alignment with Conditional Random Field


DeepFace 2: Face Alignment with Conditional Random Field – We present a novel framework to solve a large-scale face alignment problem. Our scheme consists of three phases: (1) face alignment through constraint set on the constraint, (2) face alignment through prediction on face model, and (3) face alignment through face transformation. All of our proposed framework operates on a constrained face model, and is very fast and scalable to support large-scale face alignment. We evaluated our framework on the MNIST face alignment dataset provided by Google Scholar, where it achieves competitive performance compared to a state-of-the-art bounding box fusion method and a state-of-the-art 3D face alignment method.

In this paper, we propose a simple and flexible framework of convolutional neural network (CNN) models that exploit local attention. The model constructs a representation from a set of local features in an iterative process, which are then utilized to reconstruct the target feature representation for the whole network. The main problem arising in CNNs is to estimate an attention vector for each object, while ignoring any attention between them. To overcome this problem we propose a neural network model based on a multi-scale attention mechanism. This model employs features from the local features to learn global attention, which maps each multi-scale attention vector to an attention matrix. The model can generate object representations for the target feature representation, which are used to enhance semantic representations generated by the system. We have conducted extensive experiments on an image-by-image retrieval task. The model demonstrates remarkable performance on the task of image retrieval, outperforming the previous state of the art on all the test datasets.

Paying More Attention to Proposals via Modal Attention and Action Units

Fast and Accurate Stochastic Variational Inference

DeepFace 2: Face Alignment with Conditional Random Field

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  • Learning with Variational Inference and Stochastic Gradient MCMC

    Fully-Fusion Image Restoration with Multi-Resolution Convolutional Sparse CodingIn this paper, we propose a simple and flexible framework of convolutional neural network (CNN) models that exploit local attention. The model constructs a representation from a set of local features in an iterative process, which are then utilized to reconstruct the target feature representation for the whole network. The main problem arising in CNNs is to estimate an attention vector for each object, while ignoring any attention between them. To overcome this problem we propose a neural network model based on a multi-scale attention mechanism. This model employs features from the local features to learn global attention, which maps each multi-scale attention vector to an attention matrix. The model can generate object representations for the target feature representation, which are used to enhance semantic representations generated by the system. We have conducted extensive experiments on an image-by-image retrieval task. The model demonstrates remarkable performance on the task of image retrieval, outperforming the previous state of the art on all the test datasets.


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