Abstract: The present disclosure provides a method, an apparatus, an electronic device, a storage medium and a program product for perceiving a road structure, and relates to the technical field of artificial intelligence and, in particular, to the technical field of deep learning and automatic driving. A specific implementation includes: determining, based on map data, a first perceiving result characterizing a road structure around a current position; determining, by a road structure perceiving model trained in advance, a second perceiving result characterizing the road structure around the current position; and generating a final perceiving result characterizing the road structure around the current position based on the first perceiving result and the second perceiving result.

Abstract: A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Abstract: A computer-vision method includes recognizing a feature representation of a query image depicting an unknown subject. A similarity score is computed between the representation of the query image and feature representations of a plurality of gallery images collectively depicting two or more different subjects with at least two or more gallery images for each subject, and each gallery image having a label identifying which of the subjects is depicted. One or more updated feature representations of the query image are sequentially iterated based on one or more of the computed similarity scores. For each of the one or more updated feature representations, an updated similarity score is computed between the updated feature representation and the feature representations of each of the gallery images. The unknown subject is identified based on a gallery image having a highest updated similarity score.

Abstract: Provided are a joint perception model training method, a joint perception method, a device, and a storage medium. The joint perception model training method includes: acquiring sample images and perception tags of the sample images; acquiring a preset joint perception model, where the joint perception model includes a feature extraction network and a joint perception network; performing feature extraction on the sample images through the feature extraction network to obtain target sample features; performing joint perception through the joint perception network according to the target sample features to obtain perception prediction results; and training the preset joint perception model according to the perception prediction results and the perception tags, where the joint perception includes executing at least two perception tasks.

Abstract: Provided are a method for training a region of interest (ROI) detection model, a method for detecting an ROI, a device, and a medium. The specific implementation includes: performing feature extraction on a sample image to obtain a sample feature data; performing non-linear mapping on the sample feature data to obtain a first feature data and a second feature data; determining an inter-region difference data according to the second feature data and a third feature data of the first feature data in a region associated with a label ROI; and adjusting at least one of a to-be-trained feature extraction parameter and a to-be-trained feature enhancement parameter of the ROI detection model according to the inter-region difference data and the region associated with the label ROI.

Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Abstract: The present disclosure provides a training method of a text recognition model, a text recognition method, and an apparatus, relating to the technical field of artificial intelligence, and specifically, to the technical field of deep learning and computer vision, which can be applied in scenarios such as optional character recognition, etc. The specific implementation solution is: performing mask prediction on visual features of an acquired sample image, to obtain a predicted visual feature; performing mask prediction on semantic features of acquired sample text, to obtain a predicted semantic feature, where the sample image includes text; determining a first loss value of the text of the sample image according to the predicted visual feature; determining a second loss value of the sample text according to the predicted semantic feature; training, according to the first loss value and the second loss value, to obtain the text recognition model.

Abstract: A computer-vision method includes recognizing a feature representation of a query image depicting an unknown subject. A similarity score is computed between the representation of the query image and feature representations of a plurality of gallery images collectively depicting two or more different subjects with at least two or more gallery images for each subject, and each gallery image having a label identifying which of the subjects is depicted. One or more updated feature representations of the query image are sequentially iterated based on one or more of the computed similarity scores. For each of the one or more updated feature representations, an updated similarity score is computed between the updated feature representation and the feature representations of each of the gallery images. The unknown subject is identified based on a gallery image having a highest updated similarity score.

Abstract: System and methods are directed to operations associated with an approximate nearest neighbor search engine. More specifically, a vector semantically representing content to be added to a search index may be received. The search index may include a neighborhood graph having a plurality of nodes, where each node of the plurality of nodes is associated with content in a content repository. A plurality of nodes within the search index determined to be most semantically similar to the received vector semantically representing content to be added to the search index may be identified. The node corresponding to the received vector semantically representing content to be added to the search index to the search index may be added to the search index and a listing of nearest neighbors associated with each of the of the plurality of nodes may be updated to include an identifier associated with the added node.

Abstract: A biomass granulator is described herein. In some embodiments, in the biomass granulator, the primarily molded particles extruded from the ring-shaped die are subjected to an orderly, quantitative and uniform reformation via a rotating scraper provided, as well as timely delivery of the finally molded particles out of the biomass granulator.

Abstract: Techniques for intelligent image search results summarization and browsing scheme are described. Images having visual attributes are evaluated for similarities based in part on their visual attributes. At least one preference score indicating a probability of an image to be selected into a summary is calculated for each image. Images are selected based on the similarity of the selected images to the other images and the preference scores of the selected images. A summary of the plurality of images is generated including the selected one individual image.

Abstract: A biomass granulator comprises a granulation chamber having a feed inlet and a discharge outlet and divided into a primary molding chamber, inside with a pressing wheel mechanism comprising a wheel seat having at least two symmetrical eccentric pressing rollers, and a secondary molding chamber, surrounding the outside of the primary one, by a ring-shaped die; the rollers are disposed with threads on the surface and a guide groove between adjacent threads; the die is provided outside with several scrapers taking the axis of the main shaft as the rotation axis, and the contact point of the roller and the is always between adjacent scrapers. The invention allows the primarily molded particles extruded from the die to be subjected to an orderly, quantitative and uniform reformation via a rotating scraper, and timely delivery of the finally molded particles out of the biomass granulator, thus solving the technical problems herein.

Abstract: A set of data points is divided into a plurality of subsets of data points. A set of cluster closures is generated based at least in part on the subset of data points. Each cluster closure envelopes a corresponding cluster of a set of clusters and is comprised of data points of the enveloped cluster and data points neighboring the enveloped cluster. A k-Means approximator iteratively assigns data points to a cluster of the set of clusters and updates a set of cluster centroids corresponding to the set of clusters. The k-Means approximator assigns data points based at least in part on the set of cluster closures.

Abstract: A facility for visual search on a mobile device takes advantage of multi-modal and multi-touch input on the mobile device. By extracting lexical entities from a spoken search query and matching the lexical entities to image tags, the facility provides candidate images for each entity. Selected ones of the candidate images are used to construct a composite visual query image on a query canvas. The relative size and position of the selected candidate images in the composite visual query image, which need not be an existing image, contribute to a definition of a context of the composite visual query image being submitted for context-aware visual search.

Abstract: The techniques described herein determine an initial set of ranked images associated with an image-based search query. Based on visual content similarities between images in the initial set of ranked images, the techniques select confident image samples from the initial set of ranked images. The techniques then use the confident image samples to rerank the initial set of ranked images. Accordingly, a search engine uses the confident image samples to promote images that are likely to be relevant to the search query, while demoting images that are not likely to be relevant to the search query. Therefore, the search engine can provide improved relevance-based search results to an image-based search query.

Abstract: Described is a technology for computing visual and textual summaries for tagged image collections. Heterogeneous affinity propagation is used to together identify both visual and textual exemplars. The heterogeneous affinity propagation finds the exemplars for relational heterogeneous data (e.g., images and words) by considering the relationships (e.g., similarities) within pairs of images, pairs of words, and relationships of words to images (affinity) in an integrated manner.

Abstract: Techniques for identifying a salient object with respect to its context are described. A process receives an input image that includes a salient object. The process segments the input image into multiple regions and calculates a saliency value for each of the segmented regions based on scale image levels. The process constructs saliency maps based at least in part on the calculated saliency value, and combines the saliency maps to construct a total saliency map. Next, the process connects a set of line segments computed from the input image and utilizes the total saliency map to compute a closed boundary, which forms a shape prior from the closed boundary, and extracts the salient object from the total saliency map and the shape prior.

Abstract: A method is provided in one example and includes receiving a rules template associated with an online interactive activity by a network element. The rules template includes at least one rule for defining a manner in which a process of the online interactive activity is to be conducted. The method further includes parsing the rules template to determine the at least one rule, and controlling the process of the online interactive activity in accordance with the determined at least one rule.

Abstract: Techniques for intelligent image search results summarization and browsing scheme are described. Images having visual attributes are evaluated for similarities based in part on their visual attributes. At least one preference score indicating a probability of an image to be selected into a summary is calculated for each image. Images are selected based on the similarity of the selected images to the other images and the preference scores of the selected images. A summary of the plurality of images is generated including the selected one individual image.

Abstract: Dominant color names may be extracted from an image by analyzing spatial-context of pixels contained in the image. A dominant color region may be defined by taking a double-threshold approach that addresses ambiguous color regions and a degree of confidence that each pixel belongs in the dominant color region. Affiliation maps and binary maps may be used to generate the dominant color region. Images may be converted to a saliency map, from which a region of interest may be assigned a dominant color name. Image search results may be filtered by the dominant color name associated with the image.