# Smoothing Slot Attention Iterations and Recurrences

arXiv: https://arxiv.org/abs/2508.05417 · HTML: https://mlsanity.com/paper/2508.05417/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 29 references that are in the map.
- First posted on arXiv 7 Aug 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Conditional Object-Centric Learning from Video](https://mlsanity.com/paper/2111.12594/) (2021)
- [Bridging the Gap to Real-World Object-Centric Learning](https://mlsanity.com/paper/2209.14860/) (2022)
- [SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos](https://mlsanity.com/paper/2206.07764/) (2022)
- [Illiterate DALL-E Learns to Compose](https://mlsanity.com/paper/2110.11405/) (2021)
- [SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models](https://mlsanity.com/paper/2210.05861/) (2022)
- [Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos](https://mlsanity.com/paper/2205.14065/) (2022)
- [Object-Centric Slot Diffusion](https://mlsanity.com/paper/2303.10834/) (2023)
- [SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models](https://mlsanity.com/paper/2305.11281/) (2023)
- [Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities](https://mlsanity.com/paper/2306.04829/) (2023)
- [Improving Object-centric Learning with Query Optimization](https://mlsanity.com/paper/2210.08990/) (2022)
- [Attention over learned object embeddings enables complex visual reasoning](https://mlsanity.com/paper/2012.08508/) (2020)
- [Self-supervised Object-Centric Learning for Videos](https://mlsanity.com/paper/2310.06907/) (2023)
- [SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers](https://mlsanity.com/paper/2312.00648/) (2023)
- [Temporally Consistent Object-Centric Learning by Contrasting Slots](https://mlsanity.com/paper/2412.14295/) (2024)
- [Object-Centric Video Prediction via Decoupling of Object Dynamics and Interactions](https://mlsanity.com/paper/2302.11850/) (2023)
- [PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning](https://mlsanity.com/paper/2502.07600/) (2025)
- [Slot Attention with Re-Initialization and Self-Distillation](https://mlsanity.com/paper/2507.23755/) (2025)
- [Predicting Video Slot Attention Queries from Random Slot-Feature Pairs](https://mlsanity.com/paper/2508.01345/) (2025)
- [Vector-Quantized Vision Foundation Models for Object-Centric Learning](https://mlsanity.com/paper/2502.20263/) (2025)
- [MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning](https://mlsanity.com/paper/2505.20772/) (2025)
- [Reasoning-Enhanced Object-Centric Learning for Videos](https://mlsanity.com/paper/2403.15245/) (2024)
- [Multi-Scale Fusion for Object Representation](https://mlsanity.com/paper/2410.01539/) (2024)
- [Grouped Discrete Representation for Object-Centric Learning](https://mlsanity.com/paper/2411.02299/) (2024)
- [SlotPi: Physics-informed Object-centric Reasoning Models](https://mlsanity.com/paper/2506.10778/) (2025)

## Built on by (most cited first, 6 of 6)

- [Object-Centric Vision Token Pruning for Vision Language Models](https://mlsanity.com/paper/2511.20439/) (2025)
- [Cycle Consistency in Video Object-Centric Learning](https://mlsanity.com/paper/2605.30211/) (2026)
- [Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization](https://mlsanity.com/paper/2605.31508/) (2026)
- [HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition](https://mlsanity.com/paper/2607.08249/) (2026)
- [iSEE: Object Permanence Through Self-Supervision](https://mlsanity.com/paper/2610.01201/) (2026)
- [MOSAIC: Multi-Scale Object-Centric Slot Adaptation with Image-Guided Prototype Conditioning for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2610.13547/) (2026)

## Abstract (arXiv)

Slot Attention (SA) lies at the heart of mainstream Object-Centric Learning (OCL). Image features can be aggregated into object-level representations by SA textititeratively refining cold-start query slots. For video, such aggregation proceeds by SA textitrecurrently shared across frames, with queries cold-started on the first frame while transitioned from the previous frame's slots thereafter. However, cold-start queries lack sample-specific cues thus hindering precise aggregation on image or video's first frame; Non-first frames' queries are already sample-specific thus requiring aggregation transforms different from the first frame. We address these issues with our textitSmoothSA: (1) To smooth SA iterations on image or video's first frame, we textitpreheat cold-start queries with rich input-feature information, by a tiny module self-distilled inside OCL; (2) To smooth SA recurrences across video's first and non-first frames, we textitdifferentiate the homogeneous aggregation transforms by using full and single iterations respectively. Comprehensive experiments on object discovery, recognition and visual reasoning validate our method's effectiveness. Further visual analyses illuminate the underline mechanisms. Our textitsource code, textitmodel checkpoints and textittraining logs are provided on https://github.com/Genera1Z/SmoothSA.