# Temporally Consistent Object-Centric Learning by Contrasting Slots

arXiv: https://arxiv.org/abs/2412.14295 · HTML: https://mlsanity.com/paper/2412.14295/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 66 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 30 of 58)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020)
- [Video Instance Segmentation](https://mlsanity.com/paper/1905.04804/) (2019)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Spatiotemporal Contrastive Video Representation Learning](https://mlsanity.com/paper/2008.03800/) (2020)
- [Multi-Object Representation Learning with Iterative Variational Inference](https://mlsanity.com/paper/1903.00450/) (2019)
- [Learning Correspondence from the Cycle-Consistency of Time](https://mlsanity.com/paper/1903.07593/) (2019)
- [Conditional Object-Centric Learning from Video](https://mlsanity.com/paper/2111.12594/) (2021)
- [On the Binding Problem in Artificial Neural Networks](https://mlsanity.com/paper/2012.05208/) (2020)
- [Bridging the Gap to Real-World Object-Centric Learning](https://mlsanity.com/paper/2209.14860/) (2022)
- [Contrastive Learning of Structured World Models](https://mlsanity.com/paper/1911.12247/) (2019)
- [SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos](https://mlsanity.com/paper/2206.07764/) (2022)
- [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)
- [Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects](https://mlsanity.com/paper/1806.01794/) (2018)
- [Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs](https://mlsanity.com/paper/1901.07017/) (2019)
- [Entity Abstraction in Visual Model-Based Reinforcement Learning](https://mlsanity.com/paper/1910.12827/) (2019)
- [SCALOR: Generative World Models with Scalable Object Representations](https://mlsanity.com/paper/1910.02384/) (2019)
- [Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions](https://mlsanity.com/paper/1802.10353/) (2018)
- [ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation](https://mlsanity.com/paper/2111.10265/) (2021)
- [Object Scene Representation Transformer](https://mlsanity.com/paper/2206.06922/) (2022)
- [Object-Centric Slot Diffusion](https://mlsanity.com/paper/2303.10834/) (2023)

## Built on by (most cited first, 30 of 31)

- [CTRL-O: Language-Controllable Object-Centric Visual Representation Learning](https://mlsanity.com/paper/2503.21747/) (2025)
- [Predicting Video Slot Attention Queries from Random Slot-Feature Pairs](https://mlsanity.com/paper/2508.01345/) (2025)
- [MetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning](https://mlsanity.com/paper/2505.20772/) (2025)
- [Smoothing Slot Attention Iterations and Recurrences](https://mlsanity.com/paper/2508.05417/) (2025)
- [SlotMemory: Object-Centric KV Memory for Streaming Long-Video Generation](https://mlsanity.com/paper/2605.31033/) (2026)
- [From Vicious to Virtuous Cycles: Synergistic Representation Learning for Unsupervised Video Object-Centric Learning](https://mlsanity.com/paper/2602.03390/) (2026)
- [FORLA: Federated Object-centric Representation Learning with Slot Attention](https://mlsanity.com/paper/2506.02964/) (2025)
- [Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment](https://mlsanity.com/paper/2601.01224/) (2026)
- [Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning](https://mlsanity.com/paper/2603.22758/) (2026)
- [MosaicThinker: On-Device Visual Spatial Reasoning for Embodied AI via Iterative Construction of Space Representation](https://mlsanity.com/paper/2602.07082/) (2026)
- [Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning](https://mlsanity.com/paper/2606.12601/) (2026)
- [TSA: Temporal Slot Activation for Persistent Object-Centric Video Representation](https://mlsanity.com/paper/2606.13714/) (2026)
- [Selective Synergistic Learning for Video Object-Centric Learning](https://mlsanity.com/paper/2606.15527/) (2026)
- [Rethinking Object-Centric Representations for Video Dynamics Modeling](https://mlsanity.com/paper/2606.23436/) (2026)
- [SlotMatch: Distilling Object-Centric Representations for Unsupervised Video Segmentation](https://mlsanity.com/paper/2508.03411/) (2025)
- [Object-Centric Vision Token Pruning for Vision Language Models](https://mlsanity.com/paper/2511.20439/) (2025)
- [A Hyperbolic Perspective on Hierarchical Structure in Object-Centric Scene Representations](https://mlsanity.com/paper/2603.14022/) (2026)
- [Scene-Agnostic Object-Centric Representation Learning for 3D Gaussian Splatting](https://mlsanity.com/paper/2604.09045/) (2026)
- [Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence](https://mlsanity.com/paper/2605.03650/) (2026)
- [Prompting-MammAlps: Fine-Grained Text-to-Video Retrieval for Camera-Trap Data](https://mlsanity.com/paper/2607.09876/) (2026)
- [Compositional Video Synthesis by Temporal Object-Centric Learning](https://mlsanity.com/paper/2507.20855/) (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)
- [Slot-RAE: Streamlining Object-Centric Learning via Direct Representation Auto-Encoders](https://mlsanity.com/paper/2607.11196/) (2026)
- [Persistent Object Narratives for Token-Efficient Video Language Models](https://mlsanity.com/paper/2608.04866/) (2026)
- [Learning visual representations for compositional analysis of artworks and photographs](https://mlsanity.com/paper/2608.06142/) (2026)
- [Semantic Slots for Video Object-Centric Learning](https://mlsanity.com/paper/2608.21636/) (2026)
- [SlotDiT: Object-Centric Representations for Diffusion Transformers](https://mlsanity.com/paper/2609.17414/) (2026)
- [iSEE: Object Permanence Through Self-Supervision](https://mlsanity.com/paper/2610.01201/) (2026)

## Abstract (arXiv)

Unsupervised object-centric learning from videos is a promising approach to extract structured representations from large, unlabeled collections of videos. To support downstream tasks like autonomous control, these representations must be both compositional and temporally consistent. Existing approaches based on recurrent processing often lack long-term stability across frames because their training objective does not enforce temporal consistency. In this work, we introduce a novel object-level temporal contrastive loss for video object-centric models that explicitly promotes temporal consistency. Our method significantly improves the temporal consistency of the learned object-centric representations, yielding more reliable video decompositions that facilitate challenging downstream tasks such as unsupervised object dynamics prediction. Furthermore, the inductive bias added by our loss strongly improves object discovery, leading to state-of-the-art results on both synthetic and real-world datasets, outperforming even weakly-supervised methods that leverage motion masks as additional cues.