11 citations · 21 across the 10 of their papers we have counts for
4 papers · 1 filter
Robust Instance Segmentation through Reasoning about Multi-Object Occlusion
Xiaoding Yuan, Adam Kortylewski, Yihong Sun +1
Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches t…
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
Yihong Sun, Adam Kortylewski, Alan Yuille
Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occlude…
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
Adam Kortylewski, Qing Liu, Angtian Wang +2
Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neur…
Robust Object Detection under Occlusion with Context-Aware CompositionalNets
Angtian Wang, Yihong Sun, Adam Kortylewski +1
Detecting partially occluded objects is a difficult task. Our experimental results show that deep learning approaches, such as Faster R-CNN, are not robust at object detection unde…