High-resolution representation learning (HRNets) for Semantic Segmentation
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Updated
May 25, 2019 - Python
High-resolution representation learning (HRNets) for Semantic Segmentation
High-resolution Networks for the Fully Convolutional One-Stage Object Detection (FCOS) algorithm
Multi Person Support on HRNets
Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h).
Train the HRNet model on ImageNet
Reviving Iterative Training with Mask Guidance for Interactive Segmentation.
This is an official implementation of facial landmark detection for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h). This is an official implementation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919
Reviving Iterative Training with Mask Guidance for Interactive Segmentation
[WACV2021] Foreground-aware Semantic Representations for Image Harmonization https://arxiv.org/abs/2006.00809
[CVPR2020] f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation https://arxiv.org/abs/2001.10331
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