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tl;dr / one line summary
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Sections
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Semantic Segmentation
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Key Idea: "convolutional-ization"
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Key Idea: Encoder-decoder architecture
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Key Idea #1: Convolutionalization with "à trous convolutions"
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Key Idea #2: CRF Post-Processing
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Key Idea: Multi-Scale Features
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Key Idea: Deeper with à trous convolutions
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Key Idea: À trous Spatial Pyramid Pooling
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DeepLabv3+: Failure Cases
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Motivation for Region Mutual Information Loss
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Mutual Information: Entropy View
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Mutual Information: Probability View
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Overall Loss Function
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Experimental Setup
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Datasets
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Results: PASCAL VOC 2012
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Ablation Studies: PASCAL VOC 2012
Description:
Explore a 21-minute conference talk from the University of Central Florida on Region Mutual Information Loss for Semantic Segmentation. Delve into key concepts like convolutional-ization, encoder-decoder architecture, à trous convolutions, and CRF post-processing. Learn about multi-scale features, à trous spatial pyramid pooling, and DeepLabv3+ failure cases. Understand the motivation behind Region Mutual Information Loss, examining mutual information from entropy and probability perspectives. Discover the overall loss function, experimental setup, datasets used, and results on PASCAL VOC 2012, including ablation studies. Access accompanying slides for visual aids and additional information.

Region Mutual Information Loss for Semantic Segmentation

University of Central Florida
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#Computer Science #Artificial Intelligence #Computer Vision #Semantic Segmentation #Information Theory #Mutual Information #Machine Learning #Encoder-Decoder Architecture
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tl;dr / one line summary