The present disclosure discloses a visual relationship detection method based on adaptive clustering learning, including: detecting visual objects from an input image and recognizing the visual objects to obtain context representation; embedding the context representation of pair-wise visual objects into a low-dimensional joint subspace to obtain a visual relationship sharing representation; embedding the context representation into a plurality of low-dimensional clustering subspaces, respectively, to obtain a plurality of preliminary visual relationship enhancing representation; and then performing regularization by clustering-driven attention mechanism; fusing the visual relationship sharing representations and regularized visual relationship enhancing representations with a prior distribution over the category label of visual relationship predicate, to predict visual relationship predicates by synthetic relational reasoning. The method is capable of fine-grained recognizing visual relationships of different subclasses by mining latent relationships in-between, which improves the accuracy of visual relationship detection.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a neural network architecture for performing a machine learning task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism; generating data defining a plurality of candidate graphs based on the synaptic connectivity graph; determining, for each candidate graph, a performance measure on a machine learning task of a neural network having a neural network architecture that is specified by the candidate graph; and selecting a final neural network architecture for performing the machine learning task based on the performance measures.

An example embodiment of the present techniques receives a plurality of object detections, each object detection including an identifier. A processor may detect that a threshold number of object detections with a same identifier has been exceeded. The processor may also construct a graph including at least one connected component. Each connected component includes object detections with the same identifier that do not exceed a distance threshold between each other as vertices connected by edges. The processor may also further merge vertices in the connected component to generate a merged detection.

A multiple-object tracking system includes a convolutional neural network that receives a set of images of a scene that have each been extracted from a frame of a scene. Each of the images corresponds to a detected instance of one of multiple objects that appears in the scene. The convolutional neural network computes, for each image of the set, an appearance embedding vector defining a set of distinguishing characteristics for the image, and a graph network then modifies the appearance embedding vector for each image based on determined relationships between the image and a subset of the images corresponding to detection times temporally separated from a detection time. The modified appearance embedding vectors are then used to identify subsets of the images corresponding to identical targets.

An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

A multiple-object tracking system includes a convolutional neural network that receives a set of images of a scene that have each been extracted from a frame of a scene. Each of the images corresponds to a detected instance of one of multiple objects that appears in the scene. The convolutional neural network computes, for each image of the set, an appearance embedding vector defining a set of distinguishing characteristics for the image, and a graph network then modifies the appearance embedding vector for each image based on determined relationships between the image and a subset of the images corresponding to detection times temporally separated from a detection time. The modified appearance embedding vectors are then used to identify subsets of the images corresponding to identical targets.

A processor-implemented method includes acquiring, by a processor, a first undirected graph and a second undirected graph, generating, by the processor, a first lattice for the first undirected graph and a second lattice for the second undirected graph; matching, by the processor, the first lattice and the second lattice based on a first global structure of the first lattice and a second global structure of the second lattice, the first global structure corresponding to nodes of the first undirected graph and the second global structure corresponding to nodes of the second undirected graph, and processing the first undirected graph and the second undirected graph based on a result of the matching of the first lattice and the second lattice.

Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.

The inventive subject matter is directed to an artificial intelligence intra-operative surgical guidance system and method of use. The artificial intelligence intra-operative surgical guidance system is made of a computer executing one or more automated artificial intelligence models trained on data layer datasets collections to calculate surgical decision risks, and provide intra-operative surgical guidance; and a display configured to provide visual guidance to a user.

Examples disclosed herein are relevant to systems, methods, and other technology for determining furniture compatibility. For example, graph neural networks (GNNs) that leverage relational information between furniture items in a set may be used as models to predict a compatibility score indicative of visual compatibility of furniture items across the set. In one implementation, the GNN-based model can extend the concept of a siamese network to multiple inputs and branches and use a generalized contrastive loss function. In another implementation, the GNN-based model learns both an edge function and the function that generates the compatibility score. The predicted compatibility score can be used for a variety of purposes, including furniture item recommendations.