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arxiv: 2606.00556 · v1 · pith:WD2V3CF7new · submitted 2026-05-30 · 💻 cs.CV

Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting

Panav Shah , Geet Sethi , Ashutosh Gandhe This is my paper

Pith reviewed 2026-06-28 18:45 UTC · model grok-4.3

classification 💻 cs.CV
keywords visual groundingremote sensingsegmentation refinementmodel ensembleSAM3RemoteSAMobject localization
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The pith

Two refinement pipelines and an ensemble voting strategy improve visual grounding accuracy in remote sensing by combining RemoteSAM and SAM3.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Sequential Grounding Refinement and Cluster-Aware Grounding Refinement pipelines that use RemoteSAM for initial object location estimates and SAM3 to refine the segmentations for greater spatial consistency. It further applies majority voting across six different grounding pipelines to increase robustness. These approaches tackle the difficulties of complex scenes, small objects, and scale variations in remote sensing imagery, resulting in more reliable predictions than using any single model alone.

Core claim

The proposed pipelines and ensemble approach outperform individual models by leveraging the complementary strengths of a remote-sensing-specialized grounding model and a general-purpose segmentation model, producing more accurate and spatially consistent visual grounding predictions.

What carries the argument

The Cluster-Aware Grounding Refinement (CGR) pipeline and majority-voting ensemble across multiple grounding pipelines, which integrate initial estimates from RemoteSAM with refinements from SAM3.

Load-bearing premise

RemoteSAM's initial estimates are accurate enough that SAM3 can refine them consistently without adding new errors or scale mismatches.

What would settle it

Running the ensemble on a benchmark remote sensing visual grounding dataset and finding that its accuracy is not higher than that of the single best pipeline.

Figures

Figures reproduced from arXiv: 2606.00556 by Ashutosh Gandhe, Geet Sethi, Panav Shah.

Figure 1
Figure 1. Figure 1: mIoU performance comparison of different models [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative comparison of grounding results from different methods. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of Sequential Grounding Refinement (SGR) [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Architecture of Cluster-Aware Grounding Refinement (CGR) [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Grounding results from multiple models on images from VRS Bench [ [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Visual grounding aims to locate image regions that correspond to natural language descriptions and is a key component of interpretable vision systems. In remote sensing imagery, grounding is particularly challenging due to complex scenes, small objects, and large variations in scale. Relying on a single model is often insufficient to address these diverse challenges. In this work, we propose two grounding pipelines, Sequential Grounding Refinement (SGR) and Cluster-Aware Grounding Refinement (CGR), that combine the complementary strengths of RemoteSAM, a visual grounding model specialized for remote sensing, and SAM3, a powerful general-purpose segmentation model. Our approach first uses RemoteSAM to obtain an initial estimate of object location, which is then refined using SAM3 to produce more accurate and spatially consistent segmentations. Additionally, we explore an ensemble strategy based on majority voting across six diverse grounding pipelines, each with distinct capabilities. This multi-model framework improves robustness and significantly enhances localization accuracy. Experimental results demonstrate that the proposed pipelines and ensemble approach outperform individual models, leading to more reliable and precise visual grounding predictions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes two pipelines, Sequential Grounding Refinement (SGR) and Cluster-Aware Grounding Refinement (CGR), which use RemoteSAM to generate an initial estimate of object location in remote sensing images and then refine it using SAM3 for more accurate segmentations. It additionally describes an ensemble method using majority voting across six grounding pipelines and claims that these methods outperform individual models in visual grounding tasks.

Significance. If the claimed improvements are substantiated, the work could offer a useful approach for enhancing visual grounding in remote sensing by combining a domain-specific model with a general segmentation model and using ensembles for robustness, addressing challenges like small objects and scale variations.

major comments (2)
  1. [Abstract] Abstract: The abstract states that 'Experimental results demonstrate that the proposed pipelines and ensemble approach outperform individual models' but provides no quantitative metrics, specific baselines, dataset details, or error analysis. This absence prevents evaluation of the magnitude and reliability of the claimed improvements, which is central to the paper's contribution.
  2. [Abstract] Abstract: The proposed refinement pipelines rely on the assumption that RemoteSAM's initial estimates are accurate enough for SAM3 to consistently improve spatial consistency. However, no quantitative condition (e.g., minimum initial IoU or scale tolerance) is stated, and given the abstract's mention of small objects and large scale variation, this assumption risks being violated, potentially leading to degraded performance rather than improvement.
minor comments (1)
  1. [Abstract] Abstract: The description of the ensemble as 'across six diverse grounding pipelines' does not specify what the six pipelines are or how they differ.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract and the underlying assumptions of our refinement pipelines. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract states that 'Experimental results demonstrate that the proposed pipelines and ensemble approach outperform individual models' but provides no quantitative metrics, specific baselines, dataset details, or error analysis. This absence prevents evaluation of the magnitude and reliability of the claimed improvements, which is central to the paper's contribution.

    Authors: We agree that the abstract would benefit from including key quantitative results to allow readers to assess the improvements immediately. In the revised manuscript, we will expand the abstract to report specific metrics (e.g., mean IoU gains on the evaluated remote sensing datasets), name the primary baselines (RemoteSAM and SAM3), and reference the datasets used. The full paper already contains detailed tables and error analysis; these will be summarized concisely in the abstract. revision: yes

  2. Referee: [Abstract] Abstract: The proposed refinement pipelines rely on the assumption that RemoteSAM's initial estimates are accurate enough for SAM3 to consistently improve spatial consistency. However, no quantitative condition (e.g., minimum initial IoU or scale tolerance) is stated, and given the abstract's mention of small objects and large scale variation, this assumption risks being violated, potentially leading to degraded performance rather than improvement.

    Authors: This is a fair and important point. While our experiments demonstrate net gains across the test sets (including challenging small-object cases), we did not explicitly define failure-mode thresholds for the refinement step. In the revision we will add a short paragraph in the method section stating the practical conditions under which SGR/CGR are applied (e.g., minimum initial box area and a coarse IoU check with the language prompt) and will include a brief analysis of cases where refinement may not help or could degrade results. This will make the assumptions transparent. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical pipeline combination with no derivations or fitted predictions

full rationale

The paper describes two refinement pipelines (SGR, CGR) that chain RemoteSAM initial estimates into SAM3 refinement plus a majority-vote ensemble across six pipelines. All claims rest on experimental outperformance versus individual models; the abstract and provided text contain no equations, no parameter fitting presented as prediction, and no self-citation chains invoked to justify uniqueness or ansatzes. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, mathematical axioms, or invented entities are described in the abstract; the contribution is an empirical engineering combination of two existing models.

pith-pipeline@v0.9.1-grok · 5724 in / 969 out tokens · 26245 ms · 2026-06-28T18:45:19.148414+00:00 · methodology

discussion (0)

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Reference graph

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