Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
133 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers (2312.00648v3)

Published 1 Dec 2023 in cs.CV

Abstract: Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots. Slot-based auto-encoders stand out as a prominent method for this task. Within them, crucial aspects include guiding the encoder to generate object-specific slots and ensuring the decoder utilizes them during reconstruction. This work introduces two novel techniques, (i) an attention-based self-training approach, which distills superior slot-based attention masks from the decoder to the encoder, enhancing object segmentation, and (ii) an innovative patch-order permutation strategy for autoregressive transformers that strengthens the role of slot vectors in reconstruction. The effectiveness of these strategies is showcased experimentally. The combined approach significantly surpasses prior slot-based autoencoder methods in unsupervised object segmentation, especially with complex real-world images. We provide the implementation code at https://github.com/gkakogeorgiou/spot .

Definition Search Book Streamline Icon: https://streamlinehq.com
References (75)
  1. Self-supervised object-centric learning for videos. In NeurIPS, 2023.
  2. Towards self-supervised learning of global and object-centric representations. In ICLRW, 2022.
  3. Discorying object that can move. In CVPR, 2022.
  4. Object discovery from motion-guided tokens. In CVPR, 2023.
  5. Language models are few-shot learners. In NeurIPS, 2020.
  6. Monet: Unsupervised scene decomposition and representation. arXiv preprint arXiv:1901.11390, 2019.
  7. Emerging properties in self-supervised vision transformers. In ICCV, 2021.
  8. Object representations as fixed points: Training iterative refinement algorithms with implicit differentiation. In NeurIPs, 2022.
  9. Generative pretraining from pixels. In ICML, 2020.
  10. An empirical study of training self-supervised vision transformers. In ICCV, 2021.
  11. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021.
  12. Learning from future: A novel self-training framework for semantic segmentation. In NeurIPS, 2022.
  13. SAVi++: Towards end-to-end object-centric learning from real-world videos. In NeurIPS, 2022.
  14. Genesis: Generative scene inference and sampling with object-centric latent representations. In ICLR, 2020.
  15. Attend, infer, repeat: Fast scene understanding with generative models. NeurIPs, 2016.
  16. The pascal visual object classes (voc) challenge. International journal of computer vision, 2009.
  17. Multi-object representation learning with iterative variational inference. In ICML, 2019.
  18. Kubric: A scalable dataset generator. In CVPR, 2022.
  19. Unsupervised semantic segmentation by distilling feature correspondences. In ICLR, 2022.
  20. Semantic contours from inverse detectors. In ICCV, 2011.
  21. Masked autoencoders are scalable vision learners. In CVPR, 2022.
  22. Efficient visual pretraining with contrastive detection. In ICCV, 2021.
  23. Object discovery and representation networks. In ECCV, 2022.
  24. Dorsal: Diffusion for object-centric representations of scenes et al. arXiv preprint arXiv:2306.08068, 2023.
  25. Invariant information clustering for unsupervised image classification and segmentation. In ICCV, 2019.
  26. Improving object-centric learning with query optimization. In ICLR, 2022.
  27. Object-centric slot diffusion. In NeurIPS, 2023.
  28. Clevrtex: A texture-rich benchmark for unsupervised multi-object segmentation. In NeurIPS Datasets and Benchmarks Track, 2021.
  29. Shepherding slots to objects: Towards stable and robust object-centric learning. In CVPR, 2023.
  30. Adam: A method for stochastic optimization. In ICLR, 2015.
  31. Conditional object-centric learning from video. In ICLR, 2022.
  32. Unsupervised conditional slot attention for object centric learning. arXiv preprint arXiv:2307.09437, 2023.
  33. Harold W Kuhn. The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83–97, 1955.
  34. Microsoft coco: Common objects in context. In ECCV, 2014.
  35. Space: Unsupervised object-oriented scene representation via spatial attention and decomposition. In ICLR, 2020.
  36. Cycle self-training for domain adaptation. In NeurIPS, 2021.
  37. Object-centric learning with slot attention. In NeurIPS, 2020.
  38. Learning object-centric video models by contrasting sets. arXiv preprint arXiv:2011.10287, 2020.
  39. Complex-valued autoencoders for object discovery. Transactions on Machine Learning Research, 2022.
  40. Rotating features for object discovery. NeurIPs, 2023.
  41. Instance adaptive self-training for unsupervised domain adaptation. In ECCV, 2020.
  42. Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and localization. In CVPR, 2022.
  43. Scaling open-vocabulary object detection, 2023.
  44. Unsupervised Layered Image Decomposition into Object Prototypes. In ICCV, 2021.
  45. Bridging the gap to real-world object-centric learning. In ICLR, 2023.
  46. Localizing objects with self-supervised transformers and no labels. In BMVC, 2021.
  47. Unsupervised object localization: Observing the background to discover objects. In CVPR, 2023.
  48. Illiterate dall-e learns to compose. In ICLR, 2022a.
  49. Simple unsupervised object-centric learning for complex and naturalistic videos. In NeurIPS, 2022b.
  50. Neural systematic binder. In ICLR, 2023.
  51. Exploring the role of the bottleneck in slot-based models through covariance regularization. arXiv preprint arXiv:2306.02577, 2023.
  52. Llama 2: Open foundation and fine-tuned chat models, 2023.
  53. Learning what and where: Disentangling location and identity tracking without supervision. In ICLR, 2023.
  54. Image captioners are scalable vision learners too. In NeurIPs, 2023.
  55. Unsupervised semantic segmentation by contrasting object mask proposals. In ICCV, 2021.
  56. Adaptive self-training for object detection. In ICCV Workshop, 2023.
  57. Attention is all you need. In NeurIPS, 2017.
  58. Cut and learn for unsupervised object detection and instance segmentation. arXiv preprint arXiv:2301.11320, 2023.
  59. Spatial broadcast decoder: A simple architecture for disentangled representations in VAEs. In ICLR workshops, 2019a.
  60. Spatial broadcast decoder: A simple architecture for disentangled representations in VAEs. In ICLR workshops, 2019b.
  61. Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning. In CVPR, 2021.
  62. Benchmarking unsupervised object representations for video sequences. Journal of Machine Learning Research, 2021.
  63. Self-supervised visual representation learning with semantic grouping. NeurIPs, 2022.
  64. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270–280, 1989.
  65. Slotformer: Unsupervised visual dynamics simulation with object-centric models. In ICLR, 2023a.
  66. Slotdiffusion: Object-centric generative modeling with diffusion models. In NeurIPS, 2023b.
  67. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, 2022.
  68. Billion-scale semi-supervised learning for image classification. arXiv preprint arXiv:1905.00546, 2019.
  69. St++: Make self-training work better for semi-supervised semantic segmentation. In CVPR, 2022.
  70. Interactive self-training with mean teachers for semi-supervised object detection. In CVPR, 2021.
  71. Promising or elusive? unsupervised object segmentation from real-world single images. In NeurIPS, 2022.
  72. Xlnet: Generalized autoregressive pretraining for language understanding. In NeurIPs, 2019.
  73. Object-centric learning for real-world videos by predicting temporal feature similarities. In NeurIPS, 2023.
  74. Improving semantic segmentation via efficient self-training. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
  75. Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In ECCV, 2018.
Citations (7)
View on Semantic Scholar

Summary

  • The paper presents a novel self-training framework that refines slot-attention masks by distilling sharper decoder outputs.
  • It introduces patch-order permutation in autoregressive transformers to mitigate overfitting and strengthen object representations.
  • Empirical results demonstrate significant improvements in unsupervised object segmentation on challenging datasets like COCO and MOVi-C/E.

Overview of "SPOT: Enhancing Unsupervised Object-Centric Learning"

This paper presents "SPOT," a novel framework designed to enhance unsupervised object-centric learning within slot-based autoencoders. The foundational goal of this work is to facilitate the decomposition of complex real-world scenes into interpretable object entities, or "slots," leveraging two innovative techniques: an attention-based self-training strategy and a patch-order permutation approach tailored for autoregressive transformers.

Contribution and Techniques

SPOT introduces two key methodologies:

  1. Attention-Based Self-Training: This approach distills superior slot-based attention masks from the decoder to the encoder. The primary intent is to augment object segmentation capabilities by enhancing the fidelity of object-specific slot generation. The encoder's slot-attention masks are refined by distilling sharper attention masks from the decoder side, derived through cross-attention modules in the transformer architecture. This novel self-training scheme anchors the object segmentation process more firmly to decoder outputs, leveraging their superior performance in object decomposition tasks.
  2. Patch-Order Permutation: Applied to autoregressive transformers, this strategy focuses on varying the prediction order of patches in the transformer decoder. By permuting sequences, it modifies the autoregressive prediction dynamics, ensuring more robust utilization of slot vectors during reconstruction. This permutation not only mitigates overfitting risks associated with traditional autoregressive approaches but also strengthens the supervisory signals for slot vector learning, leading to enhanced object-centric representations.

Experimental Outcomes

Empirical evaluations underscore SPOT's effectiveness, particularly in unsupervised object segmentation. The combined application of self-training and patch-order permutation significantly boosts the performance of slot-based autoencoders over prior methods, achieving superior results on complex datasets such as COCO and MOVi-C/E. Notably, SPOT achieves a marked improvement in key metrics like mean Best Overlap and mean Intersection over Union, particularly excelling in handling challenging real-world scenes.

Practical and Theoretical Implications

Practically, SPOT's advancements in unsupervised object decomposition open avenues for developing more sophisticated AI systems capable of high-fidelity scene understanding without dependence on supervised signals. Theoretically, this work emphasizes the potential of autoregressive models paired with innovative training dynamics to tackle object segmentation, a step forward from traditional MLP-based decoders.

Future Directions

Future research could explore the broader applicability of the patch-order permutation approach to other autoregressive tasks in computer vision, beyond object-centric learning. Additionally, integrating SPOT's methods with other paradigms, such as contrastive learning or video-based object segmentation, could unveil further performance benefits. The self-training mechanism’s scalability across different model architectures and data modalities also warrants exploration, potentially extending its utility and impact on various unsupervised learning tasks.

In essence, SPOT provides a robust framework that advances the efficacy and applicability of unsupervised object-centric methods, paving the way for further innovations in object segmentation and detection within the broader machine learning landscape.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown