Abstract: The present disclosure provides methods, apparatuses, devices and storage medium for predicting correlation between objects. The method can include: detecting a first object, a second object, and a third object involved in a target image, wherein the first object and the second object represent different body parts, and the third object indicates a body object; determining a joint bounding box surrounding the first object, the second object, and the third object; and predicting correlation between the first object and the second object based on a region corresponding to the joint bounding box in the target image.

Abstract: Aspects of a storage device including a memory and a controller are provided. The controller may determine to perform garbage collection on a superblock. During the garbage collection process, the controller will typically move the superblock into an erase pool for erasing the superblock. However, aspects of the disclosure are directed to a method of measuring a raw bit error rate (RBER) of the superblock prior to erasure. The measured RBER may be used to estimate a data retention time of the storage device and provide the customer with an early warning notification if a health metric of the storage devices reaches a threshold retention time.

Abstract: Provided are a face-hand correlation degree detection method and apparatus, a device, and a storage medium. The method includes that: an image to be detected is acquired; a face feature set and a hand feature set of the image to be detected are determined on the basis of a result obtained by performing face and hand detection on the image to be detected; a first interaction feature of a target face is determined on the basis of a face feature of the target face and the hand feature set; a second interaction feature of a target hand is determined on the basis of a hand feature of the target hand and the face feature set; and a correlation between the target face and the target hand is determined on the basis of the first interaction feature and the second interaction feature.

Abstract: Methods, systems, and apparatus for detecting correlated objects involved in images are provided. In one aspect, a method includes: detecting a face object, a preset body part object, and a hand object involved in an image, performing a respective correlation prediction on every two of the face object, the preset body part object, and the hand object to obtain first, second, and third correlation prediction results, segmenting the image to determine at least one body object involved in the image to determine a first body object to which the face object belongs and a second body object to which the preset body part object belongs, adjusting the first correlation prediction result based on the first body object and the second body object, and determining correlated objects involved in the image according to the adjusted first correlation prediction result and the second and third correlation prediction results.

Abstract: Provided are a stacked object recognition method, apparatus and device, and a computer storage medium. The method includes that: an image to be recognized is acquired, the image to be recognized including an object sequence formed by stacking at least one object; edge detection and semantic segmentation are performed on the object sequence based on the image to be recognized to determine an edge segmentation image of the object sequence and a semantic segmentation image of the object sequence, the edge segmentation image including edge information of each object of the object sequence and each pixel in the semantic segmentation image representing a class of the object to which the pixel belongs; and the class of each object in the object sequence is determined based on the edge segmentation image and the semantic segmentation image.

Abstract: Provided are a face-hand correlation degree detection method and apparatus, a device, and a storage medium. The method includes that: an image to be detected is acquired; a face feature set and a hand feature set of the image to be detected are determined on the basis of a result obtained by performing face and hand detection on the image to be detected; a first interaction feature of a target face is determined on the basis of a face feature of the target face and the hand feature set; a second interaction feature of a target hand is determined on the basis of a hand feature of the target hand and the face feature set; and a correlation between the target face and the target hand is determined on the basis of the first interaction feature and the second interaction feature.

Abstract: Provided are a training and detection method and apparatus of an object detection network and a device and a storage medium. The method of training an object detection network includes: obtaining, by performing object detection for images in an image data set input into the object detection network and for each of one or more objects involved in each of the images, a confidence levels that the object is predicted as each of a plurality of preset categories; for each of the objects, determining reference labeling information of the object with respect to each of the non-labeled categories; for each of the objects, determining loss information that the object is predicted as each of the preset categories; and adjusting a network parameter of the object detection network based on the loss information that each of the objects is predicted as each of the preset categories.

Abstract: A target detection method and apparatus, an electronic device and a computer-readable storage medium are provided by the embodiments of the present disclosure. The method includes: obtaining a detection result by performing a target detection on a to-be-detected image, wherein the detection result comprises a target classification to which a target object involved in the to-be-detected image belongs and position information corresponding to the target object involved in the to-be-detected image; cropping out a proposal image involving the target object from the to-be-detected image based on the position information; determining a confidence that the target object belongs to a target classification based on the proposal image; and deleting, in response to that the confidence is less than a preset threshold, an information item concerned the target object from the detection result.

Abstract: The embodiments of the present disclosure provide a method and an apparatus for determining object classification. The method may include: performing, by a target detection network, an object detection on a first image, to obtain a first classification confidence of a target object involved in the first image; obtaining an object image comprising a re-detection object from the first image, and performing, by a filter, the object detection on the object image, to determine a second classification confidence of the re-detection object; wherein the re-detection object is the target object whose first classification confidence is within a preset threshold range; correcting the first classification confidence of the re-detection object based on the second classification confidence to obtain an updated confidence; determining a classification detection result of the re-detection object based on the updated confidence.

Abstract: The present disclosure provides methods, apparatuses, devices and storage media for detecting correlated objects involved in image. The method can include: detecting a face object, a hand object and a preset body part object involved in a target image, wherein the preset body part object represents a preset connection part between a face and a hand; respectively predicting correlation between the detected face object and the detected preset body part object, and correlation between the detected preset body part object and the detected hand object, to obtain a first correlation prediction result between the face object and the preset body part object, and a second correlation prediction result between the preset body part object and the hand object; and determining correlated objects involved in the target image based on the first correlation prediction result and the second correlation prediction result.

Abstract: The present disclosure provides methods, apparatuses, devices and storage medium for predicting correlation between objects. The method can include: detecting a first object, a second object, and a third object involved in a target image, wherein the first object and the second object represent different body parts, and the third object indicates a body object; determining a joint bounding box surrounding the first object, the second object, and the third object; and predicting correlation between the first object and the second object based on a region corresponding to the joint bounding box in the target image.

Abstract: The present disclosure provides methods, apparatuses, devices and storage media for predicting correlation between objects involved in an image. According to a method, a first object and a second object involved in an acquired image are detected, where the first object and the second object represent different body parts. First weight information of the first object with respect to a target region and second weight information of the second object with respect to the target region are determined. The target region corresponds to a surrounding box for a combination of the first object and the second object. A weighted-processing is performed on the target region respectively based on the first weight information and the second weight information to obtain a first weighted feature and a second weighted feature of the target region. A correlation between the first object and the second object within the target region is predicted based on the first weighted feature and the second weighted feature.

Abstract: Methods, systems, and apparatus for detecting correlated objects involved in images are provided. In one aspect, a method includes: detecting a face object, a preset body part object, and a hand object involved in an image, performing a respective correlation prediction on every two of the face object, the preset body part object, and the hand object to obtain first, second, and third correlation prediction results, segmenting the image to determine at least one body object involved in the image to determine a first body object to which the face object belongs and a second body object to which the preset body part object belongs, adjusting the first correlation prediction result based on the first body object and the second body object, and determining correlated objects involved in the image according to the adjusted first correlation prediction result and the second and third correlation prediction results.

Abstract: Methods, apparatuses, systems, devices, and computer-readable storage media for detecting associated objects are provided. In one aspect, a method includes: detecting at least one matching object group from an image to be detected, each of the at least one matching object group including at least two target objects; and, for each of the at least one matching object group, acquiring visual information of each of the at least two target objects in the matching object group and spatial information of the at least two target objects in the matching object group, and determining whether the at least two target objects in the matching object group are associated, according to the visual information and the spatial information of the at least two target objects in the matching object group.

Abstract: Methods, devices, electronic apparatuses and storage media of processing images, training neural networks, and recognizing human body actions are provided. In one aspect, a method of image processing includes: acquiring a human body bounding box and a target key point corresponding to a target body part in an image and acquiring first correlation information between the human body bounding box and the target key point; generating a target bounding box for the target body part according to the target key point and the human body bounding box; and determining, according to the first correlation information and pre-labeled second correlation information indicating correlation between a first body part and the human body bounding box, third correlation information to indicate a correlation between the target bounding box and a first bounding box for the first target body part.

Abstract: Methods and apparatus for training neural networks and detecting correlated objects are provided. In one aspect, a method of training a neural network includes: detecting a first-class object and second-class objects in an image; generating at least one candidate object group based on the detected first-class object and second-class objects, each candidate object group including at least one first-class object and at least two second-class objects; for each candidate object group, determining a matching degree between the first-class object and each second-class object in the candidate object group based on a neural network; determining a group correlation loss of the candidate object group based on the determined matching degree, the group correlation loss being positively correlated with a matching degree between the first-class object and a non-correlated second-class object; and adjusting network parameters of the neural network based on the group correlation loss.

Abstract: Methods, apparatuses, systems, devices and computer-readable storage media for object detection are provided. In one aspect, a method includes: detecting one or more face objects and one or more body objects from an image to be processed, determining a matching relationship between a face object of the one or more face objects and a body object of the one or more body objects, and in response to determining that the body object matches the face object based on the matching relationship, determining the body object as a detected target object.

Abstract: A sequence recognition method is implemented by using a sequence recognition network. The sequence recognition network at least includes an encoder network and a decoder network. The method includes: acquiring a to-be-processed image, the to-be-processed image including a to-be-recognized object sequence; encoding the to-be-processed image by using the encoder network to obtain a first feature sequence; decoding the first feature sequence by using the decoder network to obtain a second feature sequence; and obtaining a sequence recognition result of the object sequence based on the second feature sequence, where the sequence recognition network is obtained by respectively supervising the encoder network and the decoder network.

Abstract: A disk drive is disclosed comprising a disk comprising a plurality of servo tracks defined by servo sectors, a head actuated over the disk, and control circuitry comprising a read channel. A plurality of data tracks are defined relative to the servo tracks, wherein each data track comprises a plurality of segments. The read channel is configured into a read mode in order to first read a first segment of a first data track. During the first read, a quality metric is generated at periodic points along the first segment. After the first read, the read channel is configured into a non-read mode for a predetermined interval. After the predetermined interval, the read channel is configured into the read mode in order to second read the first segment of the first data track and generate the quality metric at the periodic points along the first segment.

Abstract: Track placement on a disk of a Data Storage Device (DSD) including writing test data in a plurality of sectors in a test track on the disk. An adjacent track on the disk is written offset from the test track by an offset distance. Data is read from the test track from an Off-Track Read Capability (OTRC) position outside of the test track. An OTRC value is determined for each sector of the plurality of sectors by varying the OTRC position and determining whether the sector meets a criterion for correctly reading data from the sector. An average OTRC value and a standard deviation are calculated for the plurality of sectors. If it is determined that the average OTRC value is greater than or equal to the predetermined multiple of the standard deviation of the OTRC values, the adjacent track is rewritten at a decreased offset distance.