Search Results for author:
Found 4 papers, 2 papers with code
Robust Instance Segmentation through Reasoning about Multi-Object Occlusion
The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification.
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries.
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.
Robust Object Detection under Occlusion with Context-Aware CompositionalNets
In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.
