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Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

https://doi.org/10.5244/c.31.20

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_24openalex-snapshot

Training Object Class Detectors from Eye Tracking Data

inferredcites →https://doi.org/10.1007/978-3-319-46478-7_34openalex-snapshot

What’s the Point: Semantic Segmentation with Point Supervision

inferredcites →https://doi.org/10.1007/978-3-319-46484-8_6openalex-snapshot

Augmented Feedback in Semantic Segmentation Under Image Level Supervision

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_42openalex-snapshot

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-319-78199-0_18openalex-snapshot

Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2014.43openalex-snapshot

The Secrets of Salient Object Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2014.49openalex-snapshot

Multiscale Combinatorial Grouping

inferredcites →https://doi.org/10.1109/cvpr.2015.7298668openalex-snapshot

Is object localization for free? - Weakly-supervised learning with convolutional neural networks

inferredcites →https://doi.org/10.1109/cvpr.2015.7298780openalex-snapshot

From image-level to pixel-level labeling with Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot

Learning Deep Features for Discriminative Localization

inferredcites →https://doi.org/10.1109/cvpr.2016.80openalex-snapshot

DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/iccv.2015.209openalex-snapshot

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

inferredcites →https://doi.org/10.48550/arxiv.1412.6806openalex-snapshot

Striving for Simplicity: The All Convolutional Net

inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

inferredcites →https://doi.org/10.48550/arxiv.1701.08261openalex-snapshot

Exploiting saliency for object segmentation from image level labels

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The Study of Weakly Supervised Semantic Segmentation Algorithm Based on Explicit Pseudo-Pixel

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Box2Seg: Attention Weighted Loss and Discriminative Feature Learning for Weakly Supervised Segmentation

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Saliency Guided Self-Attention Network for Weakly and Semi-Supervised Semantic Segmentation

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Weakly-Supervised Semantic Segmentation With Regional Location Cutting and Dynamic Credible Regions Correction

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FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference

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Weakly Supervised and Semi-Supervised Semantic Segmentation Makes Full Use of a Small Amount of Labeled Data

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Image segmentation with less manual labeling effort

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Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation

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Weakly Supervised Semantic Segmentation in Aerial Imagery via Cross-Image Semantic Mining

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Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation

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A Brief Survey on Weakly Supervised Semantic Segmentation

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DASNet: Reducing Pixel-level Annotations for Instance and Semantic Segmentation

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Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation

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Fully Using Classifiers for Weakly Supervised Semantic Segmentation with Modified Cues

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Automatic Image Labelling at Pixel Level

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Geometry Constrained Weakly Supervised Object Localization

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LID 2020: The Learning from Imperfect Data Challenge Results

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Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

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Towards Single Stage Weakly Supervised Semantic Segmentation

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Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation

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Simpler Does It: Generating Semantic Labels with Objectness Guidance

inferred← citeshttps://doi.org/10.70675/053d6ac3z8c79z451cz88c8z553669be02c2openalex-snapshot

Unsupervised and weakly supervised deep learning methods for computer vision and medical imaging

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