unMORE: Unsupervised Multi-Object Segmentation via Center-Boundary Reasoning

• URL: http://arxiv.org/abs/2506.01778v1
• Date: Mon, 02 Jun 2025 15:22:51 GMT
• Title: unMORE: Unsupervised Multi-Object Segmentation via Center-Boundary Reasoning
• Authors: Yafei Yang, Zihui Zhang, Bo Yang,
• Abstract summary: Unsupervised multi-object segmentation is a challenging problem on single images.<n>In this paper, we introduce unMORE, a novel two-stage pipeline designed to identify many complex objects in real-world images.<n>Our method excels in crowded images where all baselines collapse.
• Score: 6.259786457043613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study the challenging problem of unsupervised multi-object segmentation on single images. Existing methods, which rely on image reconstruction objectives to learn objectness or leverage pretrained image features to group similar pixels, often succeed only in segmenting simple synthetic objects or discovering a limited number of real-world objects. In this paper, we introduce unMORE, a novel two-stage pipeline designed to identify many complex objects in real-world images. The key to our approach involves explicitly learning three levels of carefully defined object-centric representations in the first stage. Subsequently, our multi-object reasoning module utilizes these learned object priors to discover multiple objects in the second stage. Notably, this reasoning module is entirely network-free and does not require human labels. Extensive experiments demonstrate that unMORE significantly outperforms all existing unsupervised methods across 6 real-world benchmark datasets, including the challenging COCO dataset, achieving state-of-the-art object segmentation results. Remarkably, our method excels in crowded images where all baselines collapse.

Related papers

• Segment Concealed Objects with Incomplete Supervision [63.637733655439334]
  Incompletely-Supervised Concealed Object (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments.<n>This task remains highly challenging due to the limited supervision provided by the incompletely annotated training data.<n>In this paper, we introduce the first unified method for ISCOS to address these challenges.
  arXiv  Detail & Related papers  (2025-06-10T16:25:15Z)
• Multiple Object Stitching for Unsupervised Representation Learning [11.087735229999817]
  We propose a method, Multiple Object Stitching, to refine the unsupervised representation for multi-object images.<n>Our method provides additional object correspondences between multi-object images without human annotations.<n> Experimental results on ImageNet, CIFAR and COCO datasets demonstrate that our proposed method achieves the leading unsupervised representation performance.
  arXiv  Detail & Related papers  (2025-06-09T02:28:21Z)
• GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision [7.511342491529451]
  We study the hard problem of 3D object segmentation in complex point clouds without requiring human labels of 3D scenes for supervision.<n>By relying on the similarity of pretrained 2D features or external signals such as motion to group 3D points as objects, existing unsupervised methods are usually limited to identifying simple objects like cars or their segmented objects are often inferior due to the lack of objectness in pretrained features.
  arXiv  Detail & Related papers  (2025-04-16T04:13:53Z)
• Find your Needle: Small Object Image Retrieval via Multi-Object Attention Optimization [5.2337753974570616]
  We address the challenge of Small Object Image Retrieval (SoIR), where the goal is to retrieve images containing a specific small object, in a cluttered scene.<n>Key challenge is constructing a single image descriptor, for scalable and efficient search, that effectively represents all objects in the image.<n>We introduce Multi-object Attention Optimization (MaO), a novel retrieval framework which incorporates a dedicated multi-object pre-training phase.
  arXiv  Detail & Related papers  (2025-03-10T08:27:02Z)
• Counting Stacked Objects [57.68870743111393]
  We propose a novel 3D counting approach that decomposes the task into two complementary subproblems.<n>By combining geometric reconstruction and deep learning-based depth analysis, our method can accurately count identical objects within containers.<n>We validate our 3D Counting pipeline on diverse real-world and large-scale synthetic datasets.
  arXiv  Detail & Related papers  (2024-11-28T13:51:16Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• De-coupling and De-positioning Dense Self-supervised Learning [65.56679416475943]
  Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. We show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We demonstrate the benefits of our method on COCO and on a new challenging benchmark, OpenImage-MINI, for object classification, semantic segmentation, and object detection.
  arXiv  Detail & Related papers  (2023-03-29T18:07:25Z)
• Image Segmentation-based Unsupervised Multiple Objects Discovery [1.7674345486888503]
  Unsupervised object discovery aims to localize objects in images. We propose a fully unsupervised, bottom-up approach, for multiple objects discovery. We provide state-of-the-art results for both unsupervised class-agnostic object detection and unsupervised image segmentation.
  arXiv  Detail & Related papers  (2022-12-20T09:48:24Z)
• Self-Supervised Learning of Object Parts for Semantic Segmentation [7.99536002595393]
  We argue that self-supervised learning of object parts is a solution to this issue. Our method surpasses the state-of-the-art on three semantic segmentation benchmarks by 17%-3%.
  arXiv  Detail & Related papers  (2022-04-27T17:55:17Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)
• Unsupervised Part Discovery from Contrastive Reconstruction [90.88501867321573]
  The goal of self-supervised visual representation learning is to learn strong, transferable image representations. We propose an unsupervised approach to object part discovery and segmentation. Our method yields semantic parts consistent across fine-grained but visually distinct categories.
  arXiv  Detail & Related papers  (2021-11-11T17:59:42Z)
• Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [9.536947328412198]
  We propose a deep network for multi-object instance segmentation that is robust to occlusion. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations.
  arXiv  Detail & Related papers  (2020-12-03T17:41:55Z)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.