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: 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: Body and hand correlation method, apparatus, device, storage medium and computer program are provided. Method includes: in an image of which an image content includes a body to be correlated and a hand to be correlated, a first correlation probability between a body detection box of the body and a hand detection box of the hand is determined; a second correlation probability between the body and a wrist key point in a key point is determined based on the key point in the body detection box, the key point including the wrist key point and elbow key point belonging to the same arm; a third correlation probability between the hand detection box and the wrist key point is determined based on the wrist key point and elbow key point; and a correlation degree between the body and the hand is determined based on the first, second, and third correlation probabilities.

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: A body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

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 body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

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: Body and hand correlation method, apparatus, device, storage medium and computer program are provided. Method includes: in an image of which an image content includes a body to be correlated and a hand to be correlated, a first correlation probability between a body detection box of the body and a hand detection box of the hand is determined; a second correlation probability between the body and a wrist key point in a key point is determined based on the key point in the body detection box, the key point including the wrist key point and elbow key point belonging to the same arm; a third correlation probability between the hand detection box and the wrist key point is determined based on the wrist key point and elbow key point; and a correlation degree between the body and the hand is determined based on the first, second, and third correlation probabilities.

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, 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, 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, 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: The present disclosure relates to an image processing method and apparatus. The method comprises: performing face and body feature extraction on an image to be processed to obtain image features including face features and/or body features, and the image to be processed comprises a first image and a second image; performing a face clustering operation, according to the face features extracted from the first image, to obtain a face clustering result; performing a body clustering operation on the second image to obtain a body clustering result, according to the face clustering result and the body features extracted from the first and second images, no face feature has been extracted from the second image; and obtaining, according to the face clustering result and the body clustering result, a clustering result for the image to be processed. The method can improve the recall rate while ensuring the accuracy of the clustering result.