Microsoft Technology Licensing, LLC

Victor Manuel Fragoso Rojas

Mei Chen

Gaurav Mittal

Nikolaos Karianakis

Gabriel Takacs

Jedrzej Jakub Kozerawski

Sudipta Narayan Sinha

Chang Liu

Joseph Michael Degol

Jae Yong Lee

A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.

Image generation using adversarial attacks for imbalanced datasets

One example provides a computing system comprising a storage machine storing instructions executable by a logic machine to extract features from a source and target images to form source and target feature maps, form a correlation map comprising a plurality of similarity scores, form an initial correspondence map comprising initial mappings between pixels of the source feature map and corresponding pixels of the target feature map, refine the initial correspondence map by, for each of one or more pixels of the source feature map, for each of a plurality of candidate correspondences, inputting a four-dimensional patch into a trained scoring function, the trained scoring function being configured to output a correctness score, and selecting a refined correspondence based at least upon the correctness scores, and output a refined correspondence map comprising a refined correspondence for each of the one or more pixels of the source feature map.

Pixel correspondence via patch-based neighborhood consensus

A scale and pose estimation method for a camera system is disclosed. Camera data for a scene acquired by the camera system is received. A scale prior parameter characterizing scale of the camera system is received. A cost of a cost function is calculated for a similarity transformation. The cost of the cost function is influenced at least by the scale prior parameter. Based at least on the cost function being less than a threshold cost, an estimated scale and pose of the camera system is output based on the similarity transformation.

Prior informed pose and scale estimation

Providing a task-aware recommendation of hyperparameter configurations for a neural network architecture. First, a joint space of tasks and hyperparameter configurations are constructed using a plurality of tasks (each of which corresponds to a dataset) and a plurality of hyperparameter configurations. The joint space is used as training data to train and optimize a performance prediction network, such that for a given unseen task corresponding to one of the plurality of tasks and a given hyperparameter configuration corresponding to one of the plurality of hyperparameter configurations, the performance prediction network is configured to predict performance that is to be achieved for the unseen task using the hyperparameter configuration.

Task-aware recommendation of hyperparameter configurations

A scale and pose estimation method for a camera system is disclosed. Camera data for a scene acquired by the camera system is received. A rotation prior parameter characterizing a gravity direction is received. A scale prior parameter characterizing scale of the camera system is received. A cost of a cost function is calculated for a similarity transformation that is configured to encode a scale and pose of the camera system. The cost of the cost function is influenced by the rotation prior parameter and the scale prior parameter. A solved similarity transformation is determined upon calculating a cost for the cost function that is less than a threshold cost. An estimated scale and pose of the camera system is output based on the solved similarity transformation.

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Bellevue, WA, United States of America

Victor Manuel Fragoso Rojas