# Learning Multi-scale Features for Foreground Segmentation

arXiv: https://arxiv.org/abs/1808.01477 · HTML: https://mlsanity.com/paper/1808.01477/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2018.

## Built on by (most cited first, 27 of 27)

- [Omnimatte: Associating Objects and Their Effects in Video](https://mlsanity.com/paper/2105.06993/) (2021)
- [LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment](https://mlsanity.com/paper/2308.01686/) (2023)
- [BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction](https://mlsanity.com/paper/2101.09585/) (2021)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [Universal Background Subtraction based on Arithmetic Distribution Neural Network](https://mlsanity.com/paper/2104.08390/) (2021)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection](https://mlsanity.com/paper/2303.14679/) (2023)
- [FactorMatte: Redefining Video Matting for Re-Composition Tasks](https://mlsanity.com/paper/2211.02145/) (2022)
- [Autoencoder-based background reconstruction and foreground segmentation with background noise estimation](https://mlsanity.com/paper/2112.08001/) (2021)
- [Instance-Level Moving Object Segmentation from a Single Image with Events](https://mlsanity.com/paper/2502.12975/) (2025)
- [LangGas: Introducing Language in Selective Zero-Shot Background Subtraction for Semi-Transparent Gas Leak Detection with a New Dataset](https://mlsanity.com/paper/2503.02910/) (2025)
- [Deep Learning for Robust Motion Segmentation with Non-Static Cameras](https://mlsanity.com/paper/2102.10929/) (2021)
- [Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos](https://mlsanity.com/paper/2207.06440/) (2022)
- [Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation](https://mlsanity.com/paper/2304.09949/) (2023)
- [Dynamic Background Subtraction by Generative Neural Networks](https://mlsanity.com/paper/2202.05336/) (2022)
- [Inductive Graph Neural Networks for Moving Object Segmentation](https://mlsanity.com/paper/2305.09585/) (2023)
- [Spatio-temporal Data Augmentation for Visual Surveillance](https://mlsanity.com/paper/2101.09895/) (2021)
- [DeePaste -- Inpainting for Pasting](https://mlsanity.com/paper/2112.10600/) (2021)
- [Motion State: A New Benchmark Multiple Object Tracking](https://mlsanity.com/paper/2312.17641/) (2023)
- [Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks](https://mlsanity.com/paper/2102.01460/) (2021)
- [Performance Analysis of a Foreground Segmentation Neural Network Model](https://mlsanity.com/paper/2105.12311/) (2021)
- [CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis](https://mlsanity.com/paper/2106.03776/) (2021)
- [Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks](https://mlsanity.com/paper/2212.10417/) (2022)
- [Weakly Supervised Realtime Dynamic Background Subtraction](https://mlsanity.com/paper/2303.02857/) (2023)
- [MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation](https://mlsanity.com/paper/2402.00918/) (2024)
- [Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation](https://mlsanity.com/paper/2405.19525/) (2024)
- [Compound and Parallel Modes of Tropical Convolutional Neural Networks](https://mlsanity.com/paper/2504.06881/) (2025)

## Abstract (arXiv)

Foreground segmentation algorithms aim segmenting moving objects from the background in a robust way under various challenging scenarios. Encoder-decoder type deep neural networks that are used in this domain recently perform impressive segmentation results. In this work, we propose a novel robust encoder-decoder structure neural network that can be trained end-to-end using only a few training examples. The proposed method extends the Feature Pooling Module (FPM) of FgSegNet by introducing features fusions inside this module, which is capable of extracting multi-scale features within images; resulting in a robust feature pooling against camera motion, which can alleviate the need of multi-scale inputs to the network. Our method outperforms all existing state-of-the-art methods in CDnet2014 dataset by an average overall F-Measure of 0.9847. We also evaluate the effectiveness of our method on SBI2015 and UCSD Background Subtraction datasets. The source code of the proposed method is made available at https://github.com/lim-anggun/FgSegNet_v2 .