Search Results for author:
Found 9 papers, 2 papers with code
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
However, these prior methods do not learn noise filters by exploiting knowledge across all clients, leading to sub-optimal and inferior noise filtering performance and thus damaging training stability.
Boundary Cues for 3D Object Shape Recovery
Early work in computer vision considered a host of geometric cues for both shape reconstruction and recognition.
Intrinsic Image Transformation via Scale Space Decomposition
We introduce a new network structure for decomposing an image into its intrinsic albedo and shading.
An Approximate Shading Model with Detail Decomposition for Object Relighting
We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene.
Structured Label Inference for Visual Understanding
In this paper, we exploit this rich structure for performing graph-based inference in label space for a number of tasks: multi-label image and video classification and action detection in untrimmed videos.
Piecewise Flat Embedding for Image Segmentation
We introduce a new multi-dimensional nonlinear embedding -- Piecewise Flat Embedding (PFE) -- for image segmentation.
Learning Structured Inference Neural Networks with Label Relations
Images of scenes have various objects as well as abundant attributes, and diverse levels of visual categorization are possible.
An Approximate Shading Model for Object Relighting
With this object model, we build an object relighting system that allows an artist to select an object from an image and insert it into a 3D scene.
Non-parametric Filtering for Geometric Detail Extraction and Material Representation
Geometric detail is a universal phenomenon in real world objects.
