# F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation

arXiv: https://arxiv.org/abs/2012.02534 · HTML: https://mlsanity.com/paper/2012.02534/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 30 of 32)

- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2207.08485/) (2022)
- [Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2209.03138/) (2022)
- [Unsupervised Video Object Segmentation via Prototype Memory Network](https://mlsanity.com/paper/2209.03712/) (2022)
- [Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier](https://mlsanity.com/paper/2112.12402/) (2021)
- [Guided Slot Attention for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2303.08314/) (2023)
- [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://mlsanity.com/paper/2211.08352/) (2022)
- [Dual Prototype Attention for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2211.12036/) (2022)
- [UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model](https://mlsanity.com/paper/2305.12659/) (2023)
- [SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2311.18286/) (2023)
- [Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation](https://mlsanity.com/paper/2308.06693/) (2023)
- [Co-attention Propagation Network for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2304.03910/) (2023)
- [Event-Free Moving Object Segmentation from Moving Ego Vehicle](https://mlsanity.com/paper/2305.00126/) (2023)
- [SMITE: Segment Me In TimE](https://mlsanity.com/paper/2410.18538/) (2024)
- [Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos](https://mlsanity.com/paper/2303.01046/) (2023)
- [Boosting General Trimap-free Matting in the Real-World Image](https://mlsanity.com/paper/2405.17916/) (2024)
- [Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2501.07806/) (2025)
- [Transform-Equivariant Consistency Learning for Temporal Sentence Grounding](https://mlsanity.com/paper/2305.04123/) (2023)
- [Joint Top-Down and Bottom-Up Frameworks for 3D Visual Grounding](https://mlsanity.com/paper/2410.15615/) (2024)
- [Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2303.04376/) (2023)
- [Hierarchical Graph Pattern Understanding for Zero-Shot VOS](https://mlsanity.com/paper/2312.09525/) (2023)
- [Improving Unsupervised Video Object Segmentation via Fake Flow Generation](https://mlsanity.com/paper/2407.11714/) (2024)
- [GMOS: Grounding Moving Object Segmentation in 3D Space and Time](https://mlsanity.com/paper/2605.30352/) (2026)
- [Towards Imbalanced Motion: Part-Decoupling Network for Video Portrait Segmentation](https://mlsanity.com/paper/2307.16565/) (2023)
- [Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2309.14786/) (2023)
- [Unsupervised Region-Growing Network for Object Segmentation in Atmospheric Turbulence](https://mlsanity.com/paper/2311.03572/) (2023)
- [Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2504.05904/) (2025)
- [PCaM: A Progressive Focus Attention-Based Information Fusion Method for Improving Vision Transformer Domain Adaptation](https://mlsanity.com/paper/2506.17232/) (2025)

## Abstract (arXiv)

Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearance changing) are still not well-handled. To alleviate these issues, we propose a novel Focus on Foreground Network (F2Net), which delves into the intra-inter frame details for the foreground objects and thus effectively improve the segmentation performance. Specifically, our proposed network consists of three main parts: Siamese Encoder Module, Center Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module. Firstly, we take a siamese encoder to extract the feature representations of paired frames (reference frame and current frame). Then, a Center Guiding Appearance Diffusion Module is designed to capture the inter-frame feature (dense correspondences between reference frame and current frame), intra-frame feature (dense correspondences in current frame), and original semantic feature of current frame. Specifically, we establish a Center Prediction Branch to predict the center location of the foreground object in current frame and leverage the center point information as spatial guidance prior to enhance the inter-frame and intra-frame feature extraction, and thus the feature representation considerably focus on the foreground objects. Finally, we propose a Dynamic Information Fusion Module to automatically select relatively important features through three aforementioned different level features. Extensive experiments on DAVIS2016, Youtube-object, and FBMS datasets show that our proposed F2Net achieves the state-of-the-art performance with significant improvement.