# Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning arXiv: https://arxiv.org/abs/2303.17842 ยท HTML: https://mlsanity.com/paper/2303.17842/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 46 of its 61 references that are in the map. - First posted on arXiv 31 Mar 2023. ## Builds on (most cited first, 30 of 46) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016) - [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019) - [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016) - [Object-Centric Learning with Slot Attention](https://mlsanity.com/paper/2006.15055/) (2020) - [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) - [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019) - [Multi-Object Representation Learning with Iterative Variational Inference](https://mlsanity.com/paper/1903.00450/) (2019) - [Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/1803.10464/) (2018) - [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021) - [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020) - [Conditional Object-Centric Learning from Video](https://mlsanity.com/paper/2111.12594/) (2021) - [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016) - [GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations](https://mlsanity.com/paper/1907.13052/) (2019) - [Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations](https://mlsanity.com/paper/2111.13152/) (2021) - [Bridging the Gap to Real-World Object-Centric Learning](https://mlsanity.com/paper/2209.14860/) (2022) - [Attend, Infer, Repeat: Fast Scene Understanding with Generative Models](https://mlsanity.com/paper/1603.08575/) (2016) - [SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition](https://mlsanity.com/paper/2001.02407/) (2020) - [Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation](https://mlsanity.com/paper/1502.02734/) (2015) - [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) - [Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos](https://mlsanity.com/paper/2205.14065/) (2022) - [CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning](https://mlsanity.com/paper/1910.04744/) (2019) - [Neural Expectation Maximization](https://mlsanity.com/paper/1708.03498/) (2017) - [Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs](https://mlsanity.com/paper/1901.07017/) (2019) - [Semi-supervised Semantic Segmentation with Directional Context-aware Consistency](https://mlsanity.com/paper/2106.14133/) (2021) - [GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement](https://mlsanity.com/paper/2104.09958/) (2021) - [SCALOR: Generative World Models with Scalable Object Representations](https://mlsanity.com/paper/1910.02384/) (2019) ## Built on by (most cited first, 17 of 17) - [Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities](https://mlsanity.com/paper/2306.04829/) (2023) - [SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers](https://mlsanity.com/paper/2312.00648/) (2023) - [Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models](https://mlsanity.com/paper/2407.15589/) (2024) - [Learning to Compose: Improving Object Centric Learning by Injecting Compositionality](https://mlsanity.com/paper/2405.00646/) (2024) - [Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction](https://mlsanity.com/paper/2508.10731/) (2025) - [Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior](https://mlsanity.com/paper/2305.19550/) (2023) - [Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing](https://mlsanity.com/paper/2402.18277/) (2024) - [Bootstrapping Top-down Information for Self-modulating Slot Attention](https://mlsanity.com/paper/2411.01801/) (2024) - [Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment](https://mlsanity.com/paper/2601.01224/) (2026) - [Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning](https://mlsanity.com/paper/2310.08929/) (2023) - [OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning](https://mlsanity.com/paper/2407.02386/) (2024) - [Simplified priors for Object-Centric Learning](https://mlsanity.com/paper/2410.00728/) (2024) - [Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning](https://mlsanity.com/paper/2505.02071/) (2025) - [Disentangled Representation Learning via Modular Compositional Bias](https://mlsanity.com/paper/2510.21402/) (2025) - [ContextFusion and Bootstrap: An Effective Approach to Improve Slot Attention-Based Object-Centric Learning](https://mlsanity.com/paper/2509.02032/) (2025) - [When Slots Compete: Slot Merging in Object-Centric Learning](https://mlsanity.com/paper/2603.11246/) (2026) - [HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition](https://mlsanity.com/paper/2607.08249/) (2026) ## Abstract (arXiv) Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view images do. Hence, owing to the difficulty of applying inductive biases, OCL for single-view images remains challenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHepherding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, Attention Refining Kernel (ARK) and Intermediate Point Predictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate locations for slots to focus on to facilitate learning of object-centric representation. We also propose a weak semi-supervision approach for OCL, whilst our proposed framework can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representation and achieves strong performance across four datasets. Code is available at urlhttps://github.com/object-understanding/SLASH.