A system is described which accepts corporate and employee data from an interested company and a prospective company, and calculates a probability that the prospective company will form a successful relationship with the interested company and generate campaign data for companies for those prospective companies

Techniques are described for taking actions based on multidimensional profile matching. A system defines a multi-dimensional user space that describes users and a multi-dimensional product space that describes products. The system also generates, for a user, a user profile that represents a vector within the multi-dimensional user space and generates, for multiple products, product profiles that each represent a vector within the multi-dimensional product space. The system further maps the user profile to a recommendation vector within the multi-dimensional product space and compares the recommendation vector to vectors within the multi-dimensional product space represented by the product profiles. Based on the comparison, the system determines, from among the multiple products, a subset of the multiple products to recommend to the user and outputs an interface that includes a recommendation for the user.

Described are methods, systems, and computer-program product embodiments for selecting a face image based on a name. In some embodiments, a method includes receiving the name. Based on the name, a name vector is selected from a plurality of name vectors in a dataset that maps a plurality of names to a plurality of corresponding name vectors in a vector space, where each name vector includes representations associated with a plurality of words associated with each name. A plurality of face vectors corresponding to a plurality of face images is received. A face vector is selected from the plurality of face vectors based on a plurality of similarity scores calculated for the plurality of corresponding face vectors, where for each name vector, a similarity score is calculated based on the name vector and each face vector. The face image is output based on the selected face vector.

Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

A framework is provided for associating dense images with topics. The framework is trained utilizing images, each having multiple regions, multiple visual characteristics and multiple keyword tags associated therewith. For each region of each image, visual features are computed from the visual characteristics utilizing a convolutional neural network, and an image feature vector is generated from the visual features. The keyword tags are utilized to generate a weighted word vector for each image by calculating a weighted average of word vector representations representing keyword tags associated with the image. The image feature vector and the weighted word vector are aligned in a common embedding space and a heat map is computed for the image. Once trained, the framework can be utilized to automatically tag images and rank the relevance of images with respect to queried keywords based upon associated heat maps.

A parking map generated based on determining a plurality of object clusters by associating pixels from an image with points from a point cloud. At least a portion of the plurality of object clusters can be classified into one of a plurality of object classifications including at least a vehicle object classification. A bounding box for one or more of the plurality of object clusters classified as the vehicle object classification can be generated. The bounding box can be included as a parking space on a parking map based on a location associated with the image and/or point cloud.

A method for fine-grained object recognition in a robotic system is disclosed that includes obtaining an image of an object from an imaging device. Based on the image, a deep category-level detection neural network is used to detect pre-defined categories of objects. A feature map is generated for each pre-defined category of object detected by the deep category-level detection neural network. Embedded features are generated, based on the feature map, using a deep instance-level detection neural network corresponding to the pre-defined category of the object, wherein each pre-defined category of an object comprises a corresponding different instance-level detection neural network. An instance-level of the object is determined based on classification of the embedded features.

A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

A parking map generated based on determining a plurality of object clusters by associating pixels from an image with points from a point cloud. At least a portion of the plurality of object clusters can be classified into one of a plurality of object classifications including at least a vehicle object classification. A bounding box for one or more of the plurality of object clusters classified as the vehicle object classification can be generated. The bounding box can be included as a parking space on a parking map based on a location associated with the image and/or point cloud.