# Learning Object-Centric Representations in SAR Images with Multi-Level Feature Fusion

arXiv: https://arxiv.org/abs/2509.09298 · HTML: https://mlsanity.com/paper/2509.09298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [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)
- [Neural Expectation Maximization](https://mlsanity.com/paper/1708.03498/) (2017)
- [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)
- [Generalization and Robustness Implications in Object-Centric Learning](https://mlsanity.com/paper/2107.00637/) (2021)
- [Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames](https://mlsanity.com/paper/2302.04973/) (2023)
- [SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection](https://mlsanity.com/paper/2403.06534/) (2024)
- [Improving Cross-Modal Retrieval with Set of Diverse Embeddings](https://mlsanity.com/paper/2211.16761/) (2022)
- [Guided Slot Attention for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2303.08314/) (2023)
- [Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey](https://mlsanity.com/paper/2007.02106/) (2020)
- [Parametric Scattering Networks](https://mlsanity.com/paper/2107.09539/) (2021)
- [DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images](https://mlsanity.com/paper/2404.03595/) (2024)
- [ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild](https://mlsanity.com/paper/2501.13354/) (2025)
- [Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition](https://mlsanity.com/paper/2304.03550/) (2023)
- [Bootstrapping Top-down Information for Self-modulating Slot Attention](https://mlsanity.com/paper/2411.01801/) (2024)
- [Improving Sound Source Localization with Joint Slot Attention on Image and Audio](https://mlsanity.com/paper/2504.15118/) (2025)
- [Future Slot Prediction for Unsupervised Object Discovery in Surgical Video](https://mlsanity.com/paper/2507.01882/) (2025)
- [IRASNet: Improved Feature-Level Clutter Reduction for Domain Generalized SAR-ATR](https://mlsanity.com/paper/2409.16845/) (2024)
- [Bottom-Up Scattering Information Perception Network for SAR target recognition](https://mlsanity.com/paper/2504.04780/) (2025)

## Abstract (arXiv)

Synthetic aperture radar (SAR) images contain not only targets of interest but also complex background clutter, including terrain reflections and speckle noise. In many cases, such clutter exhibits intensity and patterns that resemble targets, leading models to extract entangled or spurious features. Such behavior undermines the ability to form clear target representations, regardless of the classifier. To address this challenge, we propose a novel object-centric learning (OCL) framework, named SlotSAR, that disentangles target representations from background clutter in SAR images without mask annotations. SlotSAR first extracts high-level semantic features from SARATR-X and low-level scattering features from the wavelet scattering network in order to obtain complementary multi-level representations for robust target characterization. We further present a multi-level slot attention module that integrates these low- and high-level features to enhance slot-wise representation distinctiveness, enabling effective OCL. Experimental results demonstrate that SlotSAR achieves state-of-the-art performance in SAR imagery by preserving structural details compared to existing OCL methods.