Provided are an identifying device, an identification method, an identification system, and a database structure capable of identifying species of organisms including animals, insects, and plants. The identifying device for identifying organisms includes a reception unit for receiving feature information including at least one of a location, date and time, image data, attribution information of the image data, and a keyword representing a feature of the organism transmitted from a user terminal, and an identification unit for identifying the species of the organism based on the feature information received by the reception unit by referring to a database in which species of organisms are stored in association with the feature information.

A method and apparatus for liveness detection, a device, and a storage medium are provided. The method for liveness detection includes: performing reconstruction processing based on an image to be detected including a target object to obtain a reconstructed image; obtaining a reconstruction error based on the reconstructed image; and obtaining a classification result of the target object based on the image to be detected and the reconstruction error, where the classification result is living or non-living.

The present disclosure describes techniques for generating a mask frame data segment corresponding to a video frame. The disclosed techniques include obtaining a frame of a video; identifying a main area of the frame using an image segmentation algorithm; and generating a mask frame data segment corresponding to the frame based on the main area of the frame, wherein the generating a mask frame data segment corresponding to the frame based on the main area of the frame further comprises generating the mask frame data segment based on a timestamp of the frame in the video, a width and a height of the main area of the frame.

An advanced driver assist system (ADAS) may obtain a video sequence including a plurality of frames captured at the vehicle, each frame corresponding to a separate stereo image including a first viewpoint image and a second viewpoint image; generate disparity information associated with a stereo image; obtain depth information associated with an object included in the stereo image based on reflected electromagnetic waves captured at the vehicle; calculate correlation information between the depth information and the disparity information based on the stereo image, the depth information and the disparity information; and correct depth values associated with the stereo image based on the disparity information and the correlation information to generate a depth image with respect to the stereo image. The ADAS may detecting the at least one object in the stereo image, based on the depth image, and may generate an output signal based on the detection.

A method for training a model, the method including: defining a primary model for identifying a class of input data based on a first characteristic of the input data; defining a secondary model for detecting a change to a second characteristic between multiple input data captured at different times; defining a forward link from an output of an intermediate layer of the secondary model to an input of an intermediate layer of the primary model; and training the primary model and the secondary model in parallel based on a training set of input data.

A method, apparatus and system for visual localization includes extracting appearance features of an image, extracting semantic features of the image, fusing the extracted appearance features and semantic features, pooling and projecting the fused features into a semantic embedding space having been trained using fused appearance and semantic features of images having known locations, computing a similarity measure between the projected fused features and embedded, fused appearance and semantic features of images, and predicting a location of the image associated with the projected, fused features. An image can include at least one image from a plurality of modalities such as a Light Detection and Ranging image, a Radio Detection and Ranging image, or a 3D Computer Aided Design modeling image, and an image from a different sensor, such as an RGB image sensor, captured from a same geo-location, which is used to determine the semantic features of the multi-modal image.

A method and apparatus for vehicle damage assessment, an electronic device, and a computer-readable storage medium are provided. The method may include: extracting, from an input image, a first feature characterizing a part of a vehicle and a second feature characterizing a damage type of the vehicle; integrating the first feature and the second feature to generate a third feature characterizing a corresponding relation between the part and the damage type; converting the third feature into a characteristic vector; and determining a damage recognition result based on the characteristic vector. According to the technical solution of the disclosure, users can rapidly and accurately learn about the damage condition of the vehicle by providing pictures or videos of the damaged vehicle, thus providing an objective basis for subsequent damage assessment, claim settlement, and repair.

The vehicle mounted perception sensor gathers environment perception data from a scene using first and second heterogeneous (different modality) sensors, at least one of the heterogeneous sensors is directable to a predetermined region of interest. A perception processor receives the environment percpetion data and performs object recognition to identify objects each with a computed confidence score. The processor assesses the confidence score vis--vis a predetermined threshold, and based on that assessment, generates an attention signal to redirect the one of the heterogeneous sensors to a region of interest identified by the other heterogeneous sensor. In this way information from one sensor primes the other sensor to increase accuracy and provide deeper knowledge about the scene and thus do a better job of object tracking in vehicular applications.

The present disclosure describes methods, devices, and storage medium for generating a natural language description for a media object. The method includes respectively processing, by a device, a media object by using a plurality of natural language description models to obtain a plurality of first feature vectors corresponding to a plurality of feature types. The device includes a memory storing instructions and a processor in communication with the memory. The method also includes fusing, by the device, the plurality of first feature to obtain a second feature vector; and generating, by the device, a natural language description for the media object according to the second feature vector, the natural language description being used for expressing the media object in natural language. The present disclosure resolves the technical problem that natural language description generated for a media object can only give an insufficiently accurate description of the media object.

The technology described in this document can be embodied in a method for generating an iris identifier. The method includes obtaining a plurality of images of an iris, and generating a binary code for each of the plurality of images of the iris, the binary code including a sequence of bits. The method also includes identifying a first pattern of bits for which bit values and bit-locations are the same across a plurality of the binary codes, generating a first index based on the first pattern of bits, and then storing the first index on a storage device in accordance with a database management system. The first index is linked to biometric information of a different modality for a corresponding user.