Since the statistical features are independent of the value and type of personal information, the trained detector is capable of identifying various types of privacy leaks and obfuscated privacy leaks.
In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics.
Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing problem in computer vision.
Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability.
We propose an end-to-end multi-task learning network for image clarity assessment and semantic segmentation simultaneously, the results of which can be guided for news cover assessment.
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination.