9 results about "Self supervision" patented technology

A method for controlling a vehicle in an environment includes generating, via a cross-attention model, a cross-attention cost volume based on a current image of the environment and a previous image of the environment in a sequence of images. The method also includes generating combined features by combining cost volume features of the cross-attention cost volume with single-frame features associated with the current image. The single-frame features may be generated via a single-frame encoding model. The method further includes generating a depth estimate of the current image based on the combined features. The method still further includes controlling an action of the vehicle based on the depth estimate.

The invention discloses an artificial intelligence robot control system fused with a world model architecture, and the system comprises a control main system which comprises a shared multi-mode backbone network module, a strategy head, and a world model head. A visual encoder, an ontology perception encoder, a text encoder and multi-mode fusion are integrated in the shared multi-mode backbone network module, and the strategy head is used for generating a current action instruction. According to the method, a self-supervision signal provided by a world model is used as an additional training constraint, the dependence on large-scale teaching data is reduced, and a strategy head generates actions based on representation rich in physical dynamic information, so that the device has the advantages that the decision is more stable when facing environmental noise or uncertainty; error accumulation in a long-range task is remarkably reduced, and compared with a traditional open-loop strategy model, the method has the advantage that the generalization ability of training out-of-distribution scenes is remarkably improved.

An apparatus to facilitate learning reliable keypoints in situ with introspective self-supervision is disclosed. The apparatus includes one or more processors to provide a view-overlapped keyframe pair from a pose graph that is generated by a visual simultaneous localization and mapping (VSLAM) process executed by the one or more processors; determine a keypoint match from the view-overlapped keyframe pair based on a keypoint detection and matching process, the keypoint match corresponding to a keypoint; calculate an inverse reliability score based on matched pixels corresponding to the keypoint match in the view-overlapped keyframe pair; identify a supervision signal associated with the keypoint match, the supervision signal comprising a keypoint reliability score of the keypoint based on a final pose output of the VSLAM process; and train a keypoint detection neural network using the keypoint match, the inverse reliability score, and the keypoint reliability score.

Methods and systems disclosed herein relate generally to systems and methods for generating visual relationship graphs that identify relationships between objects depicted in an image. A vision-language application uses transformer encoders to generate a graph structure, in which the graph structure represents a dependency between a first region and a second region of an image. The dependency indicates that a contextual representation of the first region was derived, at least in part, by processing the second region. The contextual representation identifies a predicted identity of an image object depicted in the first region. The predicted identity is determined at least in part by identifying a relationship between the first region and other data objects associated with various modalities.

The present invention discloses a self-supervised group detection method and device for large-scale scenes and multiple objects. The method includes: modeling N people in each t-frame video of a crowded multi-person scene; extracting feature information such as activity and time trajectory for each person; training an environmental perception human behavior simulator in a self-supervised manner, that is, spontaneously learning natural human behavior in a group; based on the idea of ​​causal reasoning, using the environmental perception human behavior simulator to train a relationship network to discover paired interpersonal relationships between multiple people; determining the parameters of the relationship network, and adding a network based on a fully connected layer to realize the task of artificial group detection. The device includes: a processor and a memory. Based on the idea of ​​causal reasoning, the present invention can spontaneously learn natural human behavior in a group without the need for a large amount of labeled information, thereby completing a better group detection task.

This invention discloses a self-supervised point cloud upsampling method and system based on adversarial learning, comprising the following steps: data acquisition; hybrid geometry-aware downsampling, combining random sampling and farthest point sampling to construct self-supervised pairs conforming to real-world distribution; initial upsampling, achieving global-local feature balance through three-stage iterative refinement; further upsampling, repeating the initial upsampling process within intermediate point clouds and introducing uniformity loss statistics; a detail module, extracting multi-scale geometric features stepwise through three dynamically updated EdgeConv layers and employing a GAN-based adversarial training strategy to output a fine point cloud; a detail-aware discriminator, receiving the predicted fine point cloud and the corresponding ground real point cloud as input, reshaping them into a form suitable for convolutional layers; and point cloud reconstruction, regressing the 3D point cloud shape from the point cloud feature information. Applying this invention can improve the quality and efficiency of high-precision reconstruction for different scenes.

The application provides a spatial transcriptome double self-supervision clustering analysis method and system, relates to the field of bioinformatics, and performs pretreatment on spatial transcriptome data to be analyzed to obtain sample data composed of gene expression data, cell image features and an adjacency matrix; the sample data is input into a trained double self-supervision model to perform clustering, and the clustering distribution of the sample data is obtained; according to the clustering distribution, a cell label is obtained as a final recognized cell type; the double self-supervision is self-supervision training of a linear autoencoder and training of a global target distribution supervised graph convolutional neural network encoder; through self-supervision training of the linear autoencoder and training of the global target distribution supervised graph convolutional neural network encoder, the training effect of the encoder is improved in a double self-supervision manner, and efficient and accurate classification and recognition of spatial transcriptome cells are realized.

A device, method, and non-transitory computer readable medium that for analyzing computer source code to detect code smells is disclosed. The method includes inputting, via processing circuitry, the source code and creating, via the processing circuitry, pseudo labels by a proxy task based on a vector of tokens for the source code. In addition, the method includes training, via the processing circuitry, a transformer model on the pseudo labels, as a pre-trained model that outputs a prediction of a value of tokens in the vector of tokens, and applying, via the processing circuitry, the pre-trained model to a plurality of fine-tuning models for respective downstream tasks, where each fine-tuning model is created by training the pre-trained model. The method also includes outputting, via the processing circuitry, from each fine-tuning model, an indication of whether a code smell has been detected in the source code.