# Glass Segmentation with Multi Scales and Primary Prediction Guiding

arXiv: https://arxiv.org/abs/2402.08571 · HTML: https://mlsanity.com/paper/2402.08571/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 13 Feb 2024.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning](https://mlsanity.com/paper/1903.02351/) (2019)
- [Multi-scale Interactive Network for Salient Object Detection](https://mlsanity.com/paper/2007.09062/) (2020)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection](https://mlsanity.com/paper/2203.02688/) (2022)
- [Pyramid Feature Attention Network for Saliency detection](https://mlsanity.com/paper/1903.00179/) (2019)
- [Reverse Attention for Salient Object Detection](https://mlsanity.com/paper/1807.09940/) (2018)
- [Segmenting Transparent Objects in the Wild](https://mlsanity.com/paper/2003.13948/) (2020)
- [Towards High-Resolution Salient Object Detection](https://mlsanity.com/paper/1908.07274/) (2019)
- [Highly Efficient Salient Object Detection with 100K Parameters](https://mlsanity.com/paper/2003.05643/) (2020)
- [Automatic Polyp Segmentation via Multi-scale Subtraction Network](https://mlsanity.com/paper/2108.05082/) (2021)
- [Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06227/) (2020)
- [Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected](https://mlsanity.com/paper/2305.00278/) (2023)
- [RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation](https://mlsanity.com/paper/2307.06099/) (2023)

## Built on by (most cited first, 2 of 2)

- [NubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation](https://mlsanity.com/paper/2405.11476/) (2024)
- [GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion](https://mlsanity.com/paper/2609.36844/) (2026)

## Abstract (arXiv)

Glass-like objects can be seen everywhere in our daily life which are very hard for existing methods to segment them. The properties of transparencies pose great challenges of detecting them from the chaotic background and the vague separation boundaries further impede the acquisition of their exact contours. Moving machines which ignore glasses have great risks of crashing into transparent barriers or difficulties in analysing objects reflected in the mirror, thus it is of substantial significance to accurately locate glass-like objects and completely figure out their contours. In this paper, inspired by the scale integration strategy and the refinement method, we proposed a brand-new network, named as MGNet, which consists of a Fine-Rescaling and Merging module (FRM) to improve the ability to extract spatially relationship and a Primary Prediction Guiding module (PPG) to better mine the leftover semantics from the fused features. Moreover, we supervise the model with a novel loss function with the uncertainty-aware loss to produce high-confidence segmentation maps. Unlike the existing glass segmentation models that must be trained on different settings with respect to varied datasets, our model are trained under consistent settings and has achieved superior performance on three popular public datasets. Code is available at