Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects

• URL: http://arxiv.org/abs/2412.04243v2
• Date: Mon, 10 Mar 2025 14:42:44 GMT
• Title: Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
• Authors: Yixin Zhang, Nicholas Konz, Kevin Kramer, Maciej A. Mazurowski,
• Abstract summary: This study introduces interpretable metrics quantifying object tree-likeness and textural separability.<n>On carefully controlled synthetic experiments and real-world datasets, we show that SFM performance noticeably correlates with these factors.<n>We link these failures to "textural confusion", where models misinterpret local structure as global texture, causing over-segmentation or difficulty distinguishing objects from similar backgrounds.
• Score: 13.311084447321234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image segmentation foundation models (SFMs) like Segment Anything Model (SAM) have achieved impressive zero-shot and interactive segmentation across diverse domains. However, they struggle to segment objects with certain structures, particularly those with dense, tree-like morphology and low textural contrast from their surroundings. These failure modes are crucial for understanding the limitations of SFMs in real-world applications. To systematically study this issue, we introduce interpretable metrics quantifying object tree-likeness and textural separability. On carefully controlled synthetic experiments and real-world datasets, we show that SFM performance (e.g., SAM, SAM 2, HQ-SAM) noticeably correlates with these factors. We link these failures to "textural confusion", where models misinterpret local structure as global texture, causing over-segmentation or difficulty distinguishing objects from similar backgrounds. Notably, targeted fine-tuning fails to resolve this issue, indicating a fundamental limitation. Our study provides the first quantitative framework for modeling the behavior of SFMs on challenging structures, offering interpretable insights into their segmentation capabilities.

Related papers

• LIRA: Inferring Segmentation in Large Multi-modal Models with Local Interleaved Region Assistance [56.474856189865946]
  Large multi-modal models (LMMs) struggle with inaccurate segmentation and hallucinated comprehension.<n>We propose LIRA, a framework that capitalizes on the complementary relationship between visual comprehension and segmentation.<n>LIRA achieves state-of-the-art performance in both segmentation and comprehension tasks.
  arXiv  Detail & Related papers  (2025-07-08T07:46:26Z)
• 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)
• Think Before You Segment: High-Quality Reasoning Segmentation with GPT Chain of Thoughts [64.93416171745693]
  ThinkFirst is a training-free reasoning segmentation framework. Our approach allows GPT-4o or other powerful MLLMs to generate a detailed, chain-of-thought description of an image. This summarized description is then passed to a language-instructed segmentation assistant to aid the segmentation process.
  arXiv  Detail & Related papers  (2025-03-10T16:26:11Z)
• UrbanSAM: Learning Invariance-Inspired Adapters for Segment Anything Models in Urban Construction [51.54946346023673]
  Urban morphology is inherently complex, with irregular objects of diverse shapes and varying scales. The Segment Anything Model (SAM) has shown significant potential in segmenting complex scenes. We propose UrbanSAM, a customized version of SAM specifically designed to analyze complex urban environments.
  arXiv  Detail & Related papers  (2025-02-21T04:25:19Z)
• ZISVFM: Zero-Shot Object Instance Segmentation in Indoor Robotic Environments with Vision Foundation Models [10.858627659431928]
  Service robots must effectively recognize and segment unknown objects to enhance their functionality. Traditional supervised learningbased segmentation techniques require extensive annotated datasets. This paper proposes a novel approach (ZISVFM) for solving UOIS by leveraging the powerful zero-shot capability of the segment anything model (SAM) and explicit visual representations from a selfsupervised vision transformer (ViT)
  arXiv  Detail & Related papers  (2025-02-05T15:22:20Z)
• Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes [63.966251473172036]
  The foundational model SAM has influenced multiple fields within computer vision, and its upgraded version, SAM 2, enhances capabilities in video segmentation.<n>While SAMs have demonstrated excellent performance in segmenting context-independent concepts like people, cars, and roads, they overlook more challenging context-dependent (CD) concepts, such as visual saliency, camouflage, product defects, and medical lesions.<n>We conduct a thorough quantitative evaluation of SAMs on 11 CD concepts across 2D and 3D images and videos in various visual modalities within natural, medical, and industrial scenes.
  arXiv  Detail & Related papers  (2024-12-02T08:03:56Z)
• Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models [56.89974470863207]
  This article uses the k* Distribution, a local neighborhood analysis method, to examine the learned latent space at the level of individual concepts. We introduce skewness-based true and approximate metrics for interpreting individual concepts to assess the overall quality of vision models' latent space.
  arXiv  Detail & Related papers  (2024-08-17T01:43:51Z)
• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• ASAM: Boosting Segment Anything Model with Adversarial Tuning [9.566046692165884]
  This paper introduces ASAM, a novel methodology that amplifies a foundation model's performance through adversarial tuning. We harness the potential of natural adversarial examples, inspired by their successful implementation in natural language processing. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations.
  arXiv  Detail & Related papers  (2024-05-01T00:13:05Z)
• SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints [9.238103649037951]
  We present a framework aimed at leveraging the raw output of SAM by exploiting two novel concepts called SAM-Generated Object (SGO) and SAM-Generated Boundary (SGB) Taking into account the content characteristics of SGO, we introduce the concept of object consistency to leverage segmented regions lacking semantic information. The boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
  arXiv  Detail & Related papers  (2023-12-05T03:33:47Z)
• Robustness Analysis on Foundational Segmentation Models [28.01242494123917]
  In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks. We benchmark seven state-of-the-art segmentation architectures using 2 different datasets. Our findings reveal several key insights: VFMs exhibit vulnerabilities to compression-induced corruptions, despite not outpacing all of unimodal models in robustness, multimodal models show competitive resilience in zero-shot scenarios, and VFMs demonstrate enhanced robustness for certain object categories.
  arXiv  Detail & Related papers  (2023-06-15T16:59:42Z)
• Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping [40.07070188661184]
  Weakly-Supervised Concealed Object (WSCOS) aims to segment objects well blended with surrounding environments. It is hard to distinguish concealed objects from the background due to the intrinsic similarity. We propose a new WSCOS method to address these two challenges.
  arXiv  Detail & Related papers  (2023-05-18T14:31:34Z)
• An Empirical Study on the Robustness of the Segment Anything Model (SAM) [12.128991867050487]
  The Segment Anything Model (SAM) is a foundation model for general image segmentation. In this study we conduct a comprehensive robustness investigation of SAM under diverse real-world conditions. Our experimental results demonstrate that SAM's performance generally declines under perturbed images. By customizing prompting techniques and leveraging domain knowledge based on the unique characteristics of each dataset, the model's resilience to these perturbations can be enhanced.
  arXiv  Detail & Related papers  (2023-05-10T19:06:43Z)
• 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)
• Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images [54.08240004593062]
  We propose an end-to-end multi-category instance segmentation model, which consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB) SEA module contains a simple fully convolutional semantic segmentation branch with extra supervision to strengthen the activation of interest instances on the feature map. SCMB extends the original single mask branch to trident mask branches and introduces complementary mask supervision at different scales.
  arXiv  Detail & Related papers  (2021-07-25T08:53:59Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)

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.