Some embodiments of the current disclosure disclose methods and systems for detecting out-of-distribution (ODD) data. For example, a method for detecting ODD data includes obtaining, at a neural network composed of a plurality of layers, a set of training data generated according to a distribution. Further, the method comprises generating, via a processor, a feature map by combining mapping functions corresponding to the plurality of layers into a vector of mapping function elements and mapping, by the feature map, the set of training data to a set of feature space training data in a feature space. Further, the method comprises identifying, via the processor, a hyper-ellipsoid in the feature space enclosing the feature space training data based on the generated feature map. In addition, the method comprises determining, via the processor, the first test data sample is OOD data when a mapped first test data sample in the feature space is outside the hyper-ellipsoid.

An initial noisy graph topology is obtained and an initial adjacency matrix is generated by a similarity learning component using similarity learning and a similarity metric function. An updated adjacency matrix with node embeddings is produced from the initial adjacency matrix using a graph neural network (GNN). The node embeddings are fed back to revise the similarity learning component. The generating, producing, and feeding back operations are repeated for a plurality of iterations.

A method for controlling a robot to pick up an object in various positions. The method includes: defining a plurality of reference points on the object; mapping a first camera image of the object in a known position onto a first descriptor image; identifying the descriptors of the reference points from the first descriptor image; mapping a second camera image of the object in an unknown position onto a second descriptor image; searching the identified descriptors of the reference points in the second descriptor image; ascertaining the positions of the reference points in the three-dimensional space in the unknown position from the found positions; and ascertaining a pickup pose of the object for the unknown position from the ascertained positions of the reference points.

Embodiments described herein provide an approach (referred to as “Co-training” mechanism throughout this disclosure) that jointly learns two representations of the training data, their class probabilities and low-dimensional embeddings. Specifically, two representations of each image sample are generated: a class probability produced by the classification head and a low-dimensional embedding produced by the projection head. The classification head is trained using memory-smoothed pseudo-labels, where pseudo-labels are smoothed by aggregating information from nearby samples in the embedding space. The projection head is trained using contrastive learning on a pseudo-label graph, where samples with similar pseudo-labels are encouraged to have similar embeddings.

This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.

A computing system can be configured for low-photon-count visual object classification. The computing system can include a photon detection system including one or more cells. Each of the one or more cells can include one or more photon detectors. Each of the one or more photon detectors can be configured to output photon signatures in response to a photon being incident on the one or more photon detectors. The computing system can include one or more processors and one or more memory devices storing computer-readable data. The data can include a low-photon-count classification model and one or more instructions that, when implemented, cause the one or more processors to perform operations for low-photon-count visual object recognition. The operations can include obtaining a photon signature from a photon detection system. The operations can include providing the photon signature to a low-photon-count classification model. The operations can include determining, by the low-photon-count classification model, a classification of a visual object disposed in view of the photon detection system based at least in part on the photon signature. The operations can include providing the classification as output of the low-photon-count classification model.

A method is provided. The method includes obtaining an enhanced state graph. The enhanced state graph represents a set of objects within an environment and a set of positions of the set of objects. The enhanced state graph includes a set of object nodes, a set of property nodes and a set of goal nodes to represent a set of objectives. The method also includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The set of mechanical systems is configured to interact with one or more of the set of objects within the environment. The method further includes operating the set of mechanical systems to achieve the set of objectives based on the set of instructions.

The present disclosure relates to systems, methods, and non-transitory computer readable media for efficiently and flexibly extracting reusable geometric assets from an arbitrary arrangement of vector geometry within a digital image. For example, the disclosed systems can organize vector geometry of a digital image by structuring geometric objects into groups (e.g., clusters). The disclosed systems can assign mnemonics to these groups and transform the digital image into a mnemonic sequence. Moreover, the disclosed systems can utilize various computer-implemented algorithms to identify and filter patterns within the mnemonic sequence. The disclosed systems can then generate pattern scores for these patterns and identify which patterns of geometric objects to include within a set of reusable geometric assets.

This application discloses a map display method and apparatus, where the method includes: obtaining an initial image, where a first road is included in the initial image. The initial image is divided into a first sub-image and a second sub-image, where the first road is not included in the first sub-image, and the first road is included in the second sub-image. The first sub-image is mapped to a first plane of a transition model, and the second sub-image is mapped to a second plane of the transition model, where there is an included angle between the first plane and the second plane, and the first plane is parallel to a screen of a display device. The second plane is controlled to move by means of the transition model, until the included angle is reduced from a first angle to a second angle, and a three-dimensional map display animation is obtained.

This application relates to apparatus and methods for determining confidence levels in associated data using machine learning algorithms. In some examples, a computing device may generate training graph data where each training graph connects at least two nodes by an edge, and each node represents data. The computing device may train a machine learning algorithm based on the generated training data. The computing device may then receive linked data, which associates at least two nodes, each representing data, with each other. The computing device may generate graph data based on the linking data, to provide to the machine learning algorithm as input. The computing device may then execute the machine learning algorithm on the generated graph data to generate values for each of its edges. The values may identify, for each edge, a confidence level in the connection between the two nodes for that edge.