recurrent neural networks for semantic instance segmentation
TRANSCRIPT
Recurrent Neural Networks for Semantic Instance Segmentation
Amaia Salvador Jordi Torres Xavier Giró-i-Nieto Ferran MarquésManel BaradadMíriam Bellver
Motivation
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Semantic Instance Segmentation
- Proposal-based solutions: - Hundreds/Thousands of redundant predictions- Post-processing needed (NMS)
- Holistic & class-agnostic methods- Reduced set of predictions- Separate network for semantics
Motivation
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- Our solution: - Recurrent model that sequentially predicts binary masks and
categorical labels for each object in an image.- Learns to stop once all objects have been found.- Does not need post-processing on its output.
Semantic Instance Segmentation
Model
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Model
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Pascal VOC
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Average Precision at different IoU thresholds
Pascal VOC
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Average Recall at different IoU thresholds (class-agnostic evaluation)
Pascal VOC
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CVPPP Leaves Segmentation
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Cityscapes
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Object Sorting Patterns
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Encoder ActivationsCorrelation with convolutional activations in the encoder before and after training
Encoder ActivationsActivations at the end of the encoder before and after training