Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for performing data augmentation. In some embodiments, a method of performing data augmentation by a device includes obtaining a plurality of images from a dataset. The method further includes computing, for each image of the plurality of images, a corresponding saliency map based on a gradient of a full loss function of that image. The method further includes selecting, from a subset of arrangements of a plurality of possible arrangements, a rearrangement offset that maximizes an overall saliency of a resulting image combining the plurality of images. The method further includes generating, using the rearrangement offset and a plurality of mixing ratios, a new mixed image from the plurality of images and a new mixed label from corresponding labels of the plurality of images. The method further includes augmenting the dataset with the new mixed image and the new mixed label.

Abstract: A method of personalized image retrieval includes obtaining a natural language query including a name; replacing the name in the natural language query with a generic term to provide an anonymized query and named entity information; obtaining a plurality of initial ranking scores and a plurality of attention weights corresponding to a plurality of images using a trained scoring model that inputs the anonymized query and the plurality of images; obtaining a plurality of delta scores corresponding to the plurality of images using a re-scoring model that inputs the plurality of attention weights and the named entity information; and obtaining a plurality of final ranking scores by modifying the plurality of initial ranking scores based on the plurality of delta scores. The trained scoring model performs semantic based searching and the re-scoring model determines a probability that faces detected in the plurality of images correspond to the name.

Abstract: A method for generating an output image may include obtaining an input image having a first exposure value, generating a plurality of levels that are each respectively associated with a respective representation of the input image, based on the input image, generating the output image having a second exposure value, based on a deep neural network (DNN) including a set of sub-networks and the plurality of levels, and providing the output image.

Abstract: A method of personalized image retrieval includes obtaining a natural language query including a name; replacing the name in the natural language query with a generic term to provide an anonymized query and named entity information; obtaining a plurality of initial ranking scores and a plurality of attention weights corresponding to a plurality of images using a trained scoring model that inputs the anonymized query and the plurality of images; obtaining a plurality of delta scores corresponding to the plurality of images using a re-scoring model that inputs the plurality of attention weights and the named entity information; and obtaining a plurality of final ranking scores by modifying the plurality of initial ranking scores based on the plurality of delta scores. The trained scoring model performs semantic based searching and the re-scoring model determines a probability that faces detected in the plurality of images correspond to the name.

Abstract: The present disclosure provides methods and apparatuses for probabilistic procedure planning for generating a plan based on a goal relating to an end state. In some embodiments, a method includes receiving a request from a user to generate an action plan comprising T intermediate actions between a start state and the end state. The method further includes constructing an input query matrix based on T, the start state, the end state, positional encodings, and pseudo-random noise information. The method further includes generating, using a machine learning transformer decoder, the action plan based on the input query matrix and a plurality of learnable vectors. The method further includes providing the action plan to the user. The action plan indicates a probability distribution of a plurality of distinct action sequences, to be performed by the user, that transform the start state to the end state.

Abstract: An apparatus for providing similar contents, using a neural network, includes a memory storing instructions, and a processor configured to execute the instructions to obtain a plurality of similarity values between a user query and a plurality of images, using a similarity neural network, obtain a rank of each the obtained plurality of similarity values, and provide, as a most similar image to the user query, at least one among the plurality of images that has a respective one among the plurality of similarity values that corresponds to a highest rank among the obtained rank of each of the plurality of similarity values. The similarity neural network is trained with a divergence neural network for outputting a divergence between a first distribution of first similarity values for positive pairs, among the plurality of similarity values, and a second distribution of second similarity values for negative pairs, among the plurality of similarity values.

Abstract: A method of personalized image retrieval includes obtaining a natural language query including a name; replacing the name in the natural language query with a generic term to provide an anonymized query and named entity information; obtaining a plurality of initial ranking scores and a plurality of attention weights corresponding to a plurality of images using a trained scoring model that inputs the anonymized query and the plurality of images; obtaining a plurality of delta scores corresponding to the plurality of images using a re-scoring model that inputs the plurality of attention weights and the named entity information; and obtaining a plurality of final ranking scores by modifying the plurality of initial ranking scores based on the plurality of delta scores. The trained scoring model performs semantic based searching and the re-scoring model determines a probability that faces detected in the plurality of images correspond to the name.

Abstract: A method is provided. The method includes receiving a first signal and a second signal, generating a first sequence based on the first signal by embedding at least one feature in the first signal, generating a second sequence based on the second signal by embedding at least one feature in the second signal, determining a minimum cost path of aligning the first sequence with the second sequence based on the embedded at least one feature in the first sequence and the embedded at least one feature in the second sequence, and aligning the first sequence with second sequence based on the determined minimum cost path.

Abstract: A method for generating an output image may include obtaining an input image having a first exposure value, generating a plurality of levels that are each respectively associated with a respective representation of the input image, based on the input image, generating the output image having a second exposure value, based on a deep neural network (DNN) including a set of sub-networks and the plurality of levels, and providing the output image.

Abstract: An apparatus for providing similar contents, using a neural network, includes a memory storing instructions, and a processor configured to execute the instructions to obtain a plurality of similarity values between a user query and a plurality of images, using a similarity neural network, obtain a rank of each the obtained plurality of similarity values, and provide, as a most similar image to the user query, at least one among the plurality of images that has a respective one among the plurality of similarity values that corresponds to a highest rank among the obtained rank of each of the plurality of similarity values. The similarity neural network is trained with a divergence neural network for outputting a divergence between a first distribution of first similarity values for positive pairs, among the plurality of similarity values, and a second distribution of second similarity values for negative pairs, among the plurality of similarity values.