Abstract: A foreground attentive neural network is used to learn feature representations. Discriminative features are extracted from the foreground of the input images. The discriminative features are used for various visual recognition tasks such as person re-identification and multi-target tracking. A deep neural network can include a foreground attentive subnetwork, a body part subnetwork and the feature fusion subnetwork. The foreground attentive subnetwork focuses on foreground by passing each input image through an encoder and decoder network. Then, the encoded feature maps are averagely sliced and discriminately learned in the following body part subnetwork. Afterwards, the resulting feature maps are fused in the feature fusion subnetwork. The final feature vectors are then normalized on the unit sphere space and learned by following the symmetric triplet loss layer.

Abstract: A visual recognition system to process images includes a global sub-network including a convolutional layer and a first max pooling layer. A local sub-network is connected to receive data from the global sub-network, and includes at least two convolutional layers, each connected to a max pooling layer. A fusion network is connected to receive data from the local sub-network, and includes a plurality of fully connected layers that respectively determine local feature maps derived from images. A loss layer is connected to receive data from the fusion network, set filter parameters, and minimize ranking error.

Abstract: A foreground attentive neural network is used to learn feature representations. Discriminative features are extracted from the foreground of the input images. The discriminative features are used for various visual recognition tasks such as person re-identification and multi-target tracking. A deep neural network can include a foreground attentive subnetwork, a body part subnetwork and the feature fusion subnetwork. The foreground attentive subnetwork focuses on foreground by passing each input image through an encoder and decoder network. Then, the encoded feature maps are averagely sliced and discriminately learned in the following body part subnetwork. Afterwards, the resulting feature maps are fused in the feature fusion subnetwork. The final feature vectors are then normalized on the unit sphere space and learned by following the symmetric triplet loss layer.

Abstract: A visual recognition system to process images includes a global sub-network including a convolutional layer and a first max pooling layer. A local sub-network is connected to receive data from the global sub-network, and includes at least two convolutional layers, each connected to a max pooling layer. A fusion network is connected to receive data from the local sub-network, and includes a plurality of fully connected layers that respectively determine local feature maps derived from images. A loss layer is connected to receive data from the fusion network, set filter parameters, and minimize ranking error.