Search Results for author:
Found 9 papers, 4 papers with code
Invariant Descriptors for Intrinsic Reflectance Optimization
We improve upon their model by introducing illumination invariant image descriptors: color ratios.
Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation
Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels.
Physics-based Shading Reconstruction for Intrinsic Image Decomposition
We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading).
ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition
The aim is to distinguish strong photometric effects from reflectance variations.
Color Constancy by GANs: An Experimental Survey
In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs.
Automatic Generation of Dense Non-rigid Optical Flow
There hardly exists any large-scale datasets with dense optical flow of non-rigid motion from real-world imagery as of today.
Joint Learning of Intrinsic Images and Semantic Segmentation
To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation.
Three for one and one for three: Flow, Segmentation, and Surface Normals
Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems.
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process.
