Search Results for author:
Found 7 papers, 3 papers with code
Video Creation by Demonstration
We explore a novel video creation experience, namely Video Creation by Demonstration.
Learning 3D Perception from Others' Predictions
We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector.
MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
As such, prior work has looked at unsupervised instance detection and segmentation, but in the absence of annotated boxes, it is unclear how pixels must be grouped into objects and which objects are of interest.
Ranked #2 on
Unsupervised Object Detection
on KITTI
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.
