Fig 11 - uploaded by Adam Kortylewski
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ROC curves measuring occlusion localization scores in object detection with context-aware CompositionalNets learned from pool4 of VGG (solid lines) and RB3 of ResNext using ω = 0.2. We the object is on average 50% occluded at each level of background occlusion (colored lines). Note that context-aware CompositionalNets can predict the occluded regions of the objects accurately at object detection.
Source publication
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 neural networks (DCNNs) have only limited robustness to partial occlusion. We overcome these limitations by unifying DCNNs with part-based models into Compositional Convoluti...
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