Abstract: A training system includes: a model; and a training module configured to: construct a first pair of images of at least a first portion of a first human captured at different times; construct a second pair of images of at least a second portion of a second human captured at the same time from different points of view; input the first and second pairs of images to the model; the model configured to: generate first and second reconstructed images of the at least the first portion of the first human based on the first and second pairs, respectively, and the training module is configured to selectively adjust one or more parameters of the model based on: the first reconstructed image and the second reconstructed image.

Abstract: A method includes: performing unsupervised pre-training of a model, the model including and a decoder including: obtaining a first image and a second image under different conditions or from different viewpoints; encoding, by the encoder, the first image into a representation of the first image and the second image into a representation of the second image; transforming the representation of the first image into a transformed representation; decoding, by the decoder, the transformed representation into a reconstructed image, where the transforming of the representation of the first image and the decoding of the transformed representation is based on the representation of the first image and the representation of the second image; and adjusting one or more parameters of at least one of the encoder and the decoder based on minimizing a loss; and fine-tuning the model, initialized with a set of task specific encoder parameters, for a geometric vision task.

Abstract: An autonomous system includes: a first semantic segmentation model trained based on a training dataset including images and labels for the images, the first semantic segmentation model configured to generate a first segmentation map based on an image from a camera; a second semantic segmentation model of the same type of semantic segmentation model as the first semantic segmentation model, the second semantic segmentation model configured to generate a second segmentation map based on the image from the camera; an adaptation module configured to selectively adjust one or more first parameters of the second semantic segmentation model; and a reset module configured to: determine a first total number of unique classifications included in the first segmentation map; determine a second total number of unique classifications included in the first segmentation map; and selectively reset the first parameters to previous parameters, respectively, based on the first and second total numbers.

Abstract: An interaction module includes: a first text-image interaction module configured to generate a vector representation of a first text-image pair based on an encoded representation of a reference image and an encoded representation of a text modifier, the reference image and the text modifier received from a computing device. A second text-image interaction module is configured to generate a vector representation of a second text-image pair based on the encoded representation of the text modifier and an encoded representation of a candidate target image. A compatibility module is configured to compute, based on the vector representation of the first text-image pair and the vector representation of the second text-image pair, a compatibility score for a triplet including the reference image, the text modifier, and the candidate target image. A ranking module is configured to rank a set of candidate target images including the candidate target image by compatibility scores.

Abstract: A method for training, using a plurality of training images with corresponding six degrees of freedom camera pose for a given environment and a plurality of reference images, each reference image depicting an object-of-interest in the given environment and having a corresponding two-dimensional to three-dimensional correspondence for the given environment, a neural network to provide visual localization by: for each training image, detecting and segmenting object-of-interest in the training image; generating a set of two-dimensional to two-dimensional matches between the detected and segmented objects-of-interest and corresponding reference images; generating a set of two-dimensional to three-dimensional matches from the generated set of two-dimensional to two-dimensional matches and the two-dimensional to three-dimensional correspondences corresponding to the reference images; and determining localization, for each training image, by solving a perspective-n-point problem using the generated set of two-dimens

Abstract: A method for training, using a plurality of training images with corresponding six degrees of freedom camera pose for a given environment and a plurality of reference images, each reference image depicting an object-of-interest in the given environment and having a corresponding two-dimensional to three-dimensional correspondence for the given environment, a neural network to provide visual localization by: for each training image, detecting and segmenting object-of-interest in the training image; generating a set of two-dimensional to two-dimensional matches between the detected and segmented objects-of-interest and corresponding reference images; generating a set of two-dimensional to three-dimensional matches from the generated set of two-dimensional to two-dimensional matches and the two-dimensional to three-dimensional correspondences corresponding to the reference images; and determining localization, for each training image, by solving a perspective-n-point problem using the generated set of two-dimens

Abstract: A classification method includes receiving a collection of samples, each sample comprising a multidimensional feature representation. A class label prediction for each sample in the collection is generated with one or more pretrained classifiers. For at least one iteration, each multidimensional feature representation is augmented with a respective class label prediction to form an augmented representation, a set of corrupted samples is generated from the augmented representations, and a transformation that minimizes a reconstruction error for the set of corrupted samples is learned. An adapted class label prediction for at least one of the samples in the collection is generated using the learned transformation and information is output, based on the adapted class label prediction. The method is useful in predicting labels for target samples where there is no access to source domain samples that are used to train the classifier and no access to target domain training data.

Abstract: A domain-adapted classification system and method are disclosed. The method includes mapping an input set of representations to generate an output set of representations, using a learned transformation. The input set of representations includes a set of target samples from a target domain. The input set also includes, for each of a plurality of source domains, a class representation for each of a plurality of classes. The class representations are representative of a respective set of source samples from the respective source domain labeled with a respective class. The output set of representations includes an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains. A class label is predicted for at least one of the target samples based on the output set of representations and information based on the predicted class label is output.

Abstract: A method and apparatus for classifying an image. In one example, the method may include receiving one or more images associated with a source domain and one or more images associated with a target domain, identifying one or more source domain features based on the one or more images associated with the source domain, identifying one or more target domain features based on the one or more images associated with the target domain, training a conditional maximum mean discrepancy (CMMD) engine based on a difference between the one or more source domain features and the one or more target domain features, applying the CMMD engine to the one or more images associated with the target domain to generate one or more labels for each unlabeled target image of the one or more images associated with the target domain and classifying each one of the one or more images in the target domain using the one or more labels.

Abstract: A method and apparatus for classifying an image. In one example, the method may include receiving one or more images associated with a source domain and one or more images associated with a target domain, identifying one or more source domain features based on the one or more images associated with the source domain, identifying one or more target domain features based on the one or more images associated with the target domain, training a conditional maximum mean discrepancy (CMMD) engine based on a difference between the one or more source domain features and the one or more target domain features, applying the CMMD engine to the one or more images associated with the target domain to generate one or more labels for each unlabeled target image of the one or more images associated with the target domain and classifying each one of the one or more images in the target domain using the one or more labels.

Abstract: A machine learning method operates on training instances from a plurality of domains including one or more source domains and a target domain. Each training instance is represented by values for a set of features. Domain adaptation is performed using stacked marginalized denoising autoencoding (mSDA) operating on the training instances to generate a stack of domain adaptation transform layers. Each iteration of the domain adaptation includes corrupting the training instances in accord with feature corruption probabilities that are non-uniform over at least one of the set of features and the domains. A classifier is learned on the training instances transformed using the stack of domain adaptation transform layers. Thereafter, a label prediction is generated for an input instance from the target domain represented by values for the set of features by applying the classifier to the input instance transformed using the stack of domain adaptation transform domains.

Abstract: A method for domain adaptation of samples includes receiving training samples from a plurality of domains, the plurality of domains including at least one source domain and a target domain, each training sample including values for a set of features. A domain predictor is learned on at least some of the training samples from the plurality of domains and respective domain labels. Domain adaptation is performed on the training samples using marginalized denoising autoencoding. This generates a domain adaptation transform layer (or layers) that transforms the training samples to a common adapted feature space. The domain adaptation employs the domain predictor to bias the domain adaptation towards one of the plurality of domains. Domain adapted training samples and their class labels can be used to train a classifier for prediction of class labels for unlabeled target samples that have been domain adapted with the domain adaptation transform layer(s).

Abstract: A machine learning method operates on training instances from a plurality of domains including one or more source domains and a target domain. Each training instance is represented by values for a set of features. Domain adaptation is performed using stacked marginalized denoising autoencoding (mSDA) operating on the training instances to generate a stack of domain adaptation transform layers. Each iteration of the domain adaptation includes corrupting the training instances in accord with feature corruption probabilities that are non-uniform over at least one of the set of features and the domains. A classifier is learned on the training instances transformed using the stack of domain adaptation transform layers. Thereafter, a label prediction is generated for an input instance from the target domain represented by values for the set of features by applying the classifier to the input instance transformed using the stack of domain adaptation transform domains.

Abstract: Training instances from a target domain are represented by feature vectors storing values for a set of features, and are labeled by labels from a set of labels. Both a noise marginalizing transform and a weighting of one or more source domain classifiers are simultaneously learned by minimizing the expectation of a loss function that is dependent on the feature vectors corrupted with noise represented by a noise probability density function, the labels, and the one or more source domain classifiers operating on the feature vectors corrupted with the noise. An input instance from the target domain is labeled with a label from the set of labels by operations including applying the learned noise marginalizing transform to an input feature vector representing the input instance and applying the one or more source domain classifiers weighted by the learned weighting to the input feature vector representing the input instance.

Abstract: In camera-based object labeling, boost classifier ƒT(x)=?r=1M?rhr(x) is trained to classify an image represented by feature vector x using a target domain training set DT of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets DS1, . . . , DSN acquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers hr(x) and weights ?r. The rth iteration of the AdaBoost algorithm trains candidate base classifiers hrk(x) each trained on a training set DT?DSk, and selects hr(x) from previously trained candidate base classifiers. The target domain training set DT may be expanded based on a prior estimate of the labels distribution for the target domain. The object labeling system may be a vehicle identification system, a machine vision article inspection system, or so forth.

Abstract: Methods, systems and processor-readable media for vehicle classification. In general, one or more vehicles can be scanned utilizing a laser scanner to compile data indicative of an optical profile of the vehicle(s). The optical profile associated with the vehicle(s) is then pre-processed. Particular features are extracted from the optical profile following pre-processing of the optical profile. The vehicle(s) can be then classified based on the particular features extracted from the optical feature. A segmented laser profile is treated as an image and profile features that integrate the signal in one of the two directions of the image and Fisher vectors which aggregate statistics of local “patches” of the image are computed and utilized as part of the extraction and classification process.

Abstract: A classification method includes receiving a collection of samples, each sample comprising a multidimensional feature representation. A class label prediction for each sample in the collection is generated with one or more pretrained classifiers. For at least one iteration, each multidimensional feature representation is augmented with a respective class label prediction to form an augmented representation, a set of corrupted samples is generated from the augmented representations, and a transformation that minimizes a reconstruction error for the set of corrupted samples is learned. An adapted class label prediction for at least one of the samples in the collection is generated using the learned transformation and information is output, based on the adapted class label prediction. The method is useful in predicting labels for target samples where there is no access to source domain samples that are used to train the classifier and no access to target domain training data.

Abstract: A domain-adapted classification system and method are disclosed. The method includes mapping an input set of representations to generate an output set of representations, using a learned transformation. The input set of representations includes a set of target samples from a target domain. The input set also includes, for each of a plurality of source domains, a class representation for each of a plurality of classes. The class representations are representative of a respective set of source samples from the respective source domain labeled with a respective class. The output set of representations includes an adapted representation of each of the target samples and an adapted class representation for each of the classes for each of the source domains. A class label is predicted for at least one of the target samples based on the output set of representations and information based on the predicted class label is output.

Abstract: A method for diagnosis assistance exploits similarity between a new medical case and existing medical cases and experts when embedded in a common embedding space. Different types of queries are provided for, including a query-by-cases and a query-by-experts. These may be associated with different cost structures that encourage the requester to use the query-by-cases first and seek expert assistance if this proves unsuccessful. Depending on whether the query-by-cases or query-by-experts is requested, a subset of the existing cases or experts is identified based on the similarity of their representations, in the embedding space, with a representation of the new case in the embedding space. There may then be provision for communicating the new case to a selected one or more of the subset of experts for the expert to attempt to provide a diagnosis.

Abstract: A method and system for managing print jobs is disclosed. A received print job is compared with pending print jobs and executed print jobs, wherein the pending print jobs and the executed print jobs are stored in one or more print queues associated with one or more printing systems. Thereafter, one or more pending print jobs are suspended if the one or more pending print jobs are found similar to the received print job based on the comparison; or the received print job is suspended if the received print job is found similar to one or more of the executed print jobs, based on the comparison.