The aim of intrinsic image decomposition (IID) is to recover reflectance and the shading from
a given image. As different combinations are possible, IID is an under constrained problem …

Nowadays, many visual scene understanding problems are addressed by dense prediction
networks. But pixel-wise dense annotations are very expensive (eg, for scene parsing) or …

Intrinsic image decomposition assumes that the observed color image can be decomposed
into reflectance and illumination. It is beneficial for understanding the physical world, but a …

Intrinsic image decomposition enables us to estimate the low-level features of images. Due
to the benefits it provides and the challenges it holds, intrinsic image decomposition has …

Intrinsic image decomposition (IID) aims to extract intrinsic components from natural images,
namely shading and reflectance, which is widely applied in computer vision and image …

Deep learning networks are often difficult to train if there are insufficient image samples.
Gathering real-world images tailored for a specific job takes a lot of work to perform. This …

In this thesis, we propose a method for light source estimation via intrinsic decomposition
within a Neural Radiance Field-like setting. We begin by exploring a learned method to …

In the field of actual Video Object Segmentation (VOS), traditional techniques have poor
adaptability and insufficient segmentation results. Therefore, based on existing problems, an …