[Paper note] RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

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RefineNet是一种用于语义分割的深度学习模型，通过使用残差卷积单元、多分辨率融合等技术来提高分割精度，特别是在高分辨率图像上的表现。本文介绍了RefineNet的基本原理、模型结构及其在多个数据集上的实验效果。

Conv and pooling result in the lost of finer image structure, producing low resolution segmentation.
- DeepLab solve this by atrous (or dilated) convolutions to account for larger receptive fields without downscaling the image.
- FCN and Hypercolumns exploits features from intermediate layers for generating high-resolution prediction.
Feature from all levels help to generate semantic segmentation.

Compare with standard CNN and Dilated convolutions
Single RefineNet structure
Purpose of different components in RefineNet
- RCU: finetune feature map for fusion task
- Multi-resolution fusion: scale to the same resolution and sum
- Chained residual pooling: capture background context from a large image region
- Output convolutions: another RCU. There are 3 RCU between two RefineNet blocks (2 path). The whole net also adds 2 RCU before dense softmax classifier.
Residual connection: in RefineNet unit and between RefineNet blocks

Datasets
- Person-Part 1717 training and 1818 testing, human parsing
- NYUDv2 795 training, 654 testing, RGB-D images showing interior scenes
- PASCAL VOC 2012 training/validation/test – 1464/1449/1456 (usually trained with MS COCO dataset)
- Cityscapes training/validation** 2975/500, 19 classes
- PASCAL-Context segmentation labels of the whole scene for PASCAL VOC images
- SUN-RGBD around 10,000 RGB-D indoor images, 37 classes
- ADE20K MIT 150 classes, more than 20K scene images
Measurement: IoU, pixel accuracy, mean accuracy
Augmentation: random scaling, random cropping and horizontal flipping
The result on PASCAL VOC 2012 is not better than PSPNet (see the following paper note)
Variant of model
- 4-cascaded performs best but need more time