6 papers
Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation
David Biertimpel, Sindi Shkodrani, Anil S. Baslamisli +1
Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask pre…
Physics-based Shading Reconstruction for Intrinsic Image Decomposition
Anil S. Baslamisli, Yang Liu, Sezer Karaoglu +1
We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are de…
ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition
Anil S. Baslamisli, Partha Das, Hoang-An Le +2
In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother tha…
Color Constancy by GANs: An Experimental Survey
Partha Das, Anil S. Baslamisli, Yang Liu +2
In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs. By conducting a large set of experiments on different datasets, an experim…
Joint Learning of Intrinsic Images and Semantic Segmentation
Anil S. Baslamisli, Thomas T. Groenestege, Partha Das +3
Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illuminat…
Three for one and one for three: Flow, Segmentation, and Surface Normals
Hoang-An Le, Anil S. Baslamisli, Thomas Mensink +1
Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems. In this p…