Abstract: The present invention provides a method and system for detecting changes in areas, comprising: acquiring images at different times of an area by low-altitude UAVs; extracting state features for each of backbone feature pairs; analyzing importance of each of state parts due to the state parts have different importance degrees to the whole image features under different scenes; eliminating the state features from complete features by virtue of differences in importance degrees, to obtain corresponding essential parts; determining at least one change in the area by analyzing the essential parts. The invention comprehensively considers the importance of the state parts in the input images, and the essential parts contains complete and pure information are obtained, which reflects an actual changed region.

Abstract: A computer-implemented method of image processing to identify one or more objects in an image including receiving one or more input images, wherein each input image includes one or more salient instances, wherein each salient instance is indicative of an object, identifying a plurality of key points associated with each salient instance within each input image, segmentation of salient instances in each image by utilizing the plurality of key points, wherein the key points include a centre point and peripheral points of each salient instance, and predicting one or more objects within each image based on the segmentation of each salient instance.

Abstract: A system and method for modelling task features for continual learning including a task input gateway arranged to receive a series of task samples, a task feature extraction engine arranged to process the received task samples and extract task invariant and task variant features, a replay buffer configured to store a subset of previous tasks as a representative of previous tasks that were processed by the system, a task classification engine arranged to perform a task relation process to determine task relations between tasks, wherein the task relations are based at least partly on a subset of previous tasks accessed from the replay buffer, an output module configured to output the task relations to be used on continual learning, and wherein the replay buffer is updated by storing a subset of the outputs.

Abstract: The present invention provides a method and system for detecting changes in areas, comprising: acquiring images at different times of an area by low-altitude UAVs; extracting state features for each of backbone feature pairs; analyzing importance of each of state parts due to the state parts have different importance degrees to the whole image features under different scenes; eliminating the state features from complete features by virtue of differences in importance degrees, to obtain corresponding essential parts; determining at least one change in the area by analyzing the essential parts. The invention comprehensively considers the importance of the state parts in the input images, and the essential parts contains complete and pure information are obtained, which reflects an actual changed region.