Luc Van Gool

TL;DR: In this paper, the authors define a formal framework for the representation and processing of incongruent events and derive algorithms to detect these events from different types of hierarchies, different applications and a variety of data types.

TL;DR: Deep Gradient Network (DGNet) as discussed by the authors is a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD), which decouples the task into two connected branches, i.e., a context and a texture encoder.

TL;DR: This paper proposes a recursive splitting method to segment facades into a bunch of tiles for semantic recognition, which improves the processing speed, guides visual recognition on suitable scales and renders the extraction of architectural principles easy.

TL;DR: A user study confirms that for low bitrates, this approach significantly outperforms state-of-the-art methods, saving up to 67% compared to the next-best method BPG.

TL;DR: In this paper, the authors propose an uncalibrated neural inverse rendering approach to solve the photometric stereo problem, which first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth.