Abstract: Embodiments of the present disclosure relate to methods and apparatuses for a pre-emption check procedure for a sidelink transmission in 3 GPP (3rd Generation Partnership Project) 5G networks. According to an embodiment of the present disclosure, a method performed by a user equipment (UE) includes: receiving, from another UE, information associated with a pre-emption check procedure on another reserved resource of the abovementioned UE; and making an adjustment relating to a transmission on a reserved resource of the UE according to the information.

Abstract: Machine learning object-searching methods and apparatuses are disclosed. The method comprises: selecting a state from a set of states of a target object-searching scene as a first state; obtaining a target optimal object-searching strategy whose initial state is the first state for searching for a target object; performing strategy learning by taking the target optimal object-searching strategy as a learning target to obtain an object-searching strategy by which a robot searches for the target object, and adding the obtained object-searching strategy into an object-searching strategy pool; determining whether the obtained object-searching strategy is consistent with the target optimal object-searching strategy; if yes, determining that the strategy learning in which the first state is taken as the initial state of the object-searching strategy is completed; and if not, returning to the step of selecting a state from a set of states of a target object-searching scene.

Abstract: The embodiments of the present application provide an image segmentation method, an image segmentation apparatus, and a fully convolutional network system. The method includes: acquiring a target image to be processed; acquiring image feature data of the target image; inputting the image feature data into a pre-trained target network far image segmentation to obtain an output; wherein the target network is a fully convolutional network comprising a hybrid context network structure, and the hybrid context network structure is configured to extract a plurality of reference features at a predetermined scale and fuse them into a target feature that matches a scale of a target object in a segmented image; and wherein the target network is trained with sample images containing target objects at different scales; and obtaining an image segmentation result for the target image based on the output.

Abstract: The embodiment of the present application provides a machine learning method and apparatus and an object-searching method and apparatus, which are related to the field of artificial intelligence technology and applied to the robot.

Abstract: The embodiments of the present application provide an image segmentation method, an image segmentation apparatus, and a fully convolutional network system. The method includes: acquiring a target image to be processed; acquiring image feature data of the target image; inputting the image feature data into a pre-trained target network far image segmentation to obtain an output; wherein the target network is a fully convolutional network comprising a hybrid context network structure, and the hybrid context network structure is configured to extract a plurality of reference features at a predetermined scale and fuse them into a target feature that matches a scale of a target object in a segmented image; and wherein the target network is trained with sample images containing target objects at different scales; and obtaining an image segmentation result for the target image based on the output.