Semantic Bottleneck Networks add interpretable semantic concept layers to deep networks, recovering SOTA segmentation performance with drastic channel reduction and enabling failure interpretation at over 99% accuracy for most outputs.
The cityscapes dataset for semantic urban scene understanding
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A two-stage pipeline generates pseudo masks from image-level labels to train Mask R-CNN, achieving state-of-the-art results on PASCAL VOC 2012 for weakly supervised instance segmentation.
Virtual KITTI 2 supplies synthetic clones of real KITTI driving sequences with added weather and camera variants and multi-modal ground-truth annotations for autonomous driving vision research.
Dilated affinity is jointly predicted with segmentation labels to strengthen features and support efficient label propagation refinement on benchmark datasets.
citing papers explorer
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Interpretability Beyond Classification Output: Semantic Bottleneck Networks
Semantic Bottleneck Networks add interpretable semantic concept layers to deep networks, recovering SOTA segmentation performance with drastic channel reduction and enabling failure interpretation at over 99% accuracy for most outputs.
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Where are the Masks: Instance Segmentation with Image-level Supervision
A two-stage pipeline generates pseudo masks from image-level labels to train Mask R-CNN, achieving state-of-the-art results on PASCAL VOC 2012 for weakly supervised instance segmentation.
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Virtual KITTI 2
Virtual KITTI 2 supplies synthetic clones of real KITTI driving sequences with added weather and camera variants and multi-modal ground-truth annotations for autonomous driving vision research.
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Improving Semantic Segmentation via Dilated Affinity
Dilated affinity is jointly predicted with segmentation labels to strengthen features and support efficient label propagation refinement on benchmark datasets.