![]() ![]() ![]() Second, we show that the framework leads to state-of-the- art performance on image segmentation on the ReferIt dataset. First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system. The effectiveness of the framework has been validated on three datasets. Instead of using a fixed step size, we introduce for each re- gion of the image a termination gate to dynamically determine in each inference step whether to continue extrapolating additional information from the textual description. The framework uses recurrent attentive models to fuse image and language features. We propose a generic modeling framework for two sub-tasks of LBIE: language-based image segmentation and image colorization. Given a source image and a natural language description, we want to generate a target image by editing the source im- age based on the description. The information required from the imaging system, e.g.We investigate the problem of Language-Based Image Editing (LBIE) in this work. Can be done manually or automatically with an appropriate software package Selection of an area of interest and eliminating unwanted data. The size of the smallest object or distance between two objects that must exist before the imaging system will record that object or objects as separate entities. When an image is automatically improved because the program has changed due to a previous imaging experienceĪ method of highlighting areas of a specific shape within an image The number of times a specific value occurs in an image ![]() The highlighting of a straight line or edge of an object to visually increase the sharpness of the imageĪ method of mathematically changing data, e.g. The density difference between two adjacent areas on the imageĪn image comprised of discrete areas or pixels The reduction in size (in bytes) of an image to save storage space The intensity values of the individual pixels in an image, the lower the brightness the darker the image Represents a quantity changing in steps which are continuous, i.e. To produce an excellent image to maximise diagnostic accuracy ![]()
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