# Guided Slot Attention for Unsupervised Video Object Segmentation

arXiv: https://arxiv.org/abs/2303.08314 · HTML: https://mlsanity.com/paper/2303.08314/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 44 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [PVT v2: Improved Baselines with Pyramid Vision Transformer](https://mlsanity.com/paper/2106.13797/) (2021)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [F3Net: Fusion, Feedback and Focus for Salient Object Detection](https://mlsanity.com/paper/1911.11445/) (2019)
- [Self-supervised Video Object Segmentation by Motion Grouping](https://mlsanity.com/paper/2104.07658/) (2021)
- [See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks](https://mlsanity.com/paper/2001.06810/) (2020)
- [Reconstruction Network for Video Captioning](https://mlsanity.com/paper/1803.11438/) (2018)
- [SegFlow: Joint Learning for Video Object Segmentation and Optical Flow](https://mlsanity.com/paper/1709.06750/) (2017)
- [Full-Duplex Strategy for Video Object Segmentation](https://mlsanity.com/paper/2108.03151/) (2021)
- [Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2207.08485/) (2022)
- [Learning Discriminative Feature with CRF for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2008.01270/) (2020)
- [Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2209.03138/) (2022)
- [F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2012.02534/) (2020)
- [Unsupervised Video Object Segmentation via Prototype Memory Network](https://mlsanity.com/paper/2209.03712/) (2022)
- [SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition](https://mlsanity.com/paper/2009.06138/) (2020)
- [Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier](https://mlsanity.com/paper/2112.12402/) (2021)
- [Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation](https://mlsanity.com/paper/2112.08949/) (2021)
- [Global Weighted Average Pooling Bridges Pixel-level Localization and Image-level Classification](https://mlsanity.com/paper/1809.08264/) (2018)

## Built on by (most cited first, 19 of 19)

- [Segment Any Motion in Videos](https://mlsanity.com/paper/2503.22268/) (2025)
- [RoMo: Robust Motion Segmentation Improves Structure from Motion](https://mlsanity.com/paper/2411.18650/) (2024)
- [Object-Centric World Model for Language-Guided Manipulation](https://mlsanity.com/paper/2503.06170/) (2025)
- [Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2501.07806/) (2025)
- [Slot-BERT: Self-supervised Object Discovery in Surgical Video](https://mlsanity.com/paper/2501.12477/) (2025)
- [Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation](https://mlsanity.com/paper/2503.03492/) (2025)
- [Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning](https://mlsanity.com/paper/2603.22758/) (2026)
- [Rethinking Object-Centric Representations for Video Dynamics Modeling](https://mlsanity.com/paper/2606.23436/) (2026)
- [TransFlow: Motion Knowledge Transfer from Video Diffusion Models to Video Salient Object Detection](https://mlsanity.com/paper/2507.19789/) (2025)
- [Slot Attention-based Feature Filtering for Few-Shot Learning](https://mlsanity.com/paper/2508.09699/) (2025)
- [GMOS: Grounding Moving Object Segmentation in 3D Space and Time](https://mlsanity.com/paper/2605.30352/) (2026)
- [Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2309.14786/) (2023)
- [Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2504.05904/) (2025)
- [DepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2507.19790/) (2025)
- [Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion](https://mlsanity.com/paper/2509.09298/) (2025)
- [Glass Surface Detection: Leveraging Reflection Dynamics in Flash/No-flash Imagery](https://mlsanity.com/paper/2511.16887/) (2025)
- [Unsupervised Structural Scene Decomposition via Foreground-Aware Slot Attention with Pseudo-Mask Guidance](https://mlsanity.com/paper/2512.02685/) (2025)
- [Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training](https://mlsanity.com/paper/2512.06864/) (2025)
- [CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2604.14630/) (2026)

## Abstract (arXiv)

Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground--background separation. The foreground and background slots, which are initialized with query guidance, are iteratively refined based on interactions with template information. Furthermore, to improve slot--template interaction and effectively fuse global and local features in the target and reference frames, K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally, we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.