mlsanityData to 7 Oct 2026

Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

arXiv 1806.01794 ↗First posted 5 Jun 2018cs.LG cs.CV stat.MLcited by 128

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Abstract (arXiv)

We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. This is achieved by explicitly encoding object presence, locations and appearances in the latent variables of the model. SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016), including learning in an unsupervised manner, and addresses its shortcomings. We use a moving multi-MNIST dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how SQAIR overcomes them by leveraging temporal consistency of objects. Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.

Key facts. Cited by 128 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 42; 2022: 32; 2023: 23; 2024: 17; 2025: 7; 2026: 7.

128 papers.

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  1. 2021Conditional Object-Centric Learning from Videocited by 247
  2. 2022Bridging the Gap to Real-World Object-Centric Learningcited by 219
  3. 2021Towards Causal Representation Learningcited by 207
  4. 2022SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videoscited by 165
  5. 2021Illiterate DALL-E Learns to Composecited by 147
  6. 2022SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Modelscited by 146
  7. 2022Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videoscited by 139
  8. 2021GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinementcited by 109
  9. 2021Unsupervised Discovery of Object Radiance Fieldscited by 100
  10. 2021Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentationcited by 88
  11. 2023SlotDiffusion: Object-Centric Generative Modeling with Diffusion Modelscited by 85
  12. 2021Generalization and Robustness Implications in Object-Centric Learningcited by 81
38 more
  1. 2023Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similaritiescited by 78
  2. 2021Neural Production Systems: Learning Rule-Governed Visual Dynamicscited by 75
  3. 2021Is Attention Better Than Matrix Decomposition?cited by 75
  4. 2021Unsupervised Learning of Compositional Energy Conceptscited by 71
  5. 2021SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decompositioncited by 69
  6. 2021Unsupervised Part Discovery from Contrastive Reconstructioncited by 57
  7. 2023Invariant Slot Attention: Object Discovery with Slot-Centric Reference Framescited by 51
  8. 2021Opening up Open-World Trackingcited by 50
  9. 2021Unsupervised Layered Image Decomposition into Object Prototypescited by 50
  10. 2023An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learningcited by 49
  11. 2023Self-supervised Object-Centric Learning for Videoscited by 49
  12. 2022Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motioncited by 42
  13. 2021Learning Object-Centric Representations of Multi-Object Scenes from Multiple Viewscited by 37
  14. 2022Neural Systematic Bindercited by 37
  15. 2021MarioNette: Self-Supervised Sprite Learningcited by 35
  16. 2021From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligencecited by 35
  17. 2024Adaptive Slot Attention: Object Discovery with Dynamic Slot Numbercited by 34
  18. 2023OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environmentscited by 30
  19. 2024Temporally Consistent Object-Centric Learning by Contrasting Slotscited by 30
  20. 2021Unsupervised Object Learning via Common Fatecited by 29
  21. 2022Unsupervised Discovery and Composition of Object Light Fieldscited by 28
  22. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  23. 2022Compositional Scene Representation Learning via Reconstruction: A Surveycited by 27
  24. 2021Generative Video Transformer: Can Objects be the Words?cited by 24
  25. 2022OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Cloudscited by 24
  26. 2021Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofingcited by 23
  27. 2023Towards Unsupervised Object Detection From LiDAR Point Cloudscited by 22
  28. 2021Structured World Belief for Reinforcement Learning in POMDPcited by 21
  29. 2022Learning What and Where: Disentangling Location and Identity Tracking Without Supervisioncited by 21
  30. 2023Slot-VAE: Object-Centric Scene Generation with Slot Attentioncited by 20
  31. 2021Unsupervised Object-Based Transition Models for 3D Partially Observable Environmentscited by 19
  32. 2022Unsupervised Image Representation Learning with Deep Latent Particlescited by 19
  33. 2022Unsupervised Multi-object Segmentation by Predicting Probable Motion Patternscited by 19
  34. 2023Object-Centric Video Prediction via Decoupling of Object Dynamics and Interactionscited by 19
  35. 2022Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformerscited by 18
  36. 2022Neural Groundplans: Persistent Neural Scene Representations from a Single Imagecited by 17
  37. 2022Boosting Object Representation Learning via Motion and Object Continuitycited by 17
  38. 2024Slot State Space Modelscited by 17

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