arxiv: 2604.18201 · v1 · submitted 2026-04-20 · 💻 cs.CV · cs.LG

Recognition: unknown

DiffuSAM: Diffusion Guided Zero-Shot Object Grounding for Remote Sensing Imagery

Geet Sethi , Panav Shah , Ashutosh Gandhe , Soumitra Darshan Nayak

Authors on Pith no claims yet

Pith reviewed 2026-05-10 05:00 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords diffusion modelszero-shot object groundingremote sensing imageryobject localizationsegmentation modelsbounding boxeshybrid pipeline

0 comments

The pith

A hybrid pipeline using diffusion localization cues with segmentation models achieves higher accuracy for zero-shot object grounding in remote sensing imagery.

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

The paper tries to establish that diffusion models can supply localization cues which, when integrated with segmentation models, produce more accurate bounding boxes for objects in remote sensing images from text descriptions alone. This would matter because remote sensing scenes are often intricate and varied, making precise object identification from prompts useful for tasks like land monitoring or event response where existing approaches frequently underperform. The work shows the pipeline generates adaptive cues through diffusion and refines them via segmentation for improved results. Experiments report gains of over 14 percent in the Acc@0.5 measure over prior methods. If the claim holds, it demonstrates a way to pair generative diffusion strengths with segmentation for better performance in domain-specific vision problems.

Core claim

The paper claims that integrating diffusion-based localization cues with state-of-the-art segmentation models creates a robust and adaptive method for zero-shot object grounding in remote sensing imagery, leading to over a 14% increase in Acc@0.5 compared to existing state-of-the-art methods.

What carries the argument

The hybrid pipeline that generates localization cues from diffusion models and fuses them with outputs from foundational segmentation models to produce refined bounding boxes.

If this is right

Object localization becomes more reliable and adaptive across complex remote sensing scenes.
Zero-shot grounding works from text prompts without task-specific training data.
Accuracy rises by over 14% in the Acc@0.5 metric over prior approaches.
Bounding boxes are obtained more effectively in varied image conditions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The cue-fusion strategy could extend to object grounding in other image domains with limited labeled examples.
It might reduce dependence on large custom datasets for training detectors in remote sensing applications.
Different fusion weights between diffusion cues and segmentation outputs could be tested for further gains.

Load-bearing premise

Diffusion-generated localization cues are sufficiently accurate and complementary to segmentation model outputs without introducing errors that degrade results in complex or varied remote sensing scenes.

What would settle it

An evaluation on remote sensing object grounding benchmarks where the hybrid pipeline shows no accuracy improvement or performs below the segmentation model alone.

Figures

Figures reproduced from arXiv: 2604.18201 by Ashutosh Gandhe, Geet Sethi, Panav Shah, Soumitra Darshan Nayak.

**Figure 1.** Figure 1: DiffuSAM Pipeline 2 METHODOLOGY 2.1 OVERVIEW The proposed DiffuSAM pipeline integrates diffusion-based image editing with foundational segmentation models to perform text-guided object localization. Given an input image and a textual description of the target object, the pipeline generates an approximate region of interest and iteratively refines it to obtain an accurate bounding box. 2.2 PRE-PROCESSING … view at source ↗

**Figure 2.** Figure 2: Successful localization by DiffuSAM for the prompt: “The tennis court located on the right side of the image with a blue playing surface.” [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

read the original abstract

Diffusion models have emerged as powerful tools for a wide range of vision tasks, including text-guided image generation and editing. In this work, we explore their potential for object grounding in remote sensing imagery. We propose a hybrid pipeline that integrates diffusion-based localization cues with state-of-the-art segmentation models such as RemoteSAM and SAM3 to obtain more accurate bounding boxes. By leveraging the complementary strengths of generative diffusion models and foundational segmentation models, our approach enables robust and adaptive object localization across complex scenes. Experiments demonstrate that our pipeline significantly improves localization performance, achieving over a 14% increase in Acc@0.5 compared to existing state-of-the-art methods.

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.

Desk Editor's Note private letter to a colleague

The paper offers a hybrid diffusion-plus-SAM pipeline for zero-shot grounding in remote sensing and claims a 14% accuracy gain, but the abstract supplies almost no evidence to evaluate that claim.

read the letter

The main takeaway is that this paper presents a hybrid pipeline called DiffuSAM that uses diffusion models to provide localization cues for zero-shot object grounding in remote sensing imagery, combined with models like RemoteSAM and SAM3. It claims this leads to over 14% better performance on Acc@0.5 than state-of-the-art methods. What is new is the specific integration for the remote sensing domain, where imagery has unique challenges like high resolution and varied object scales. The approach leverages the generative capabilities of diffusion for cues and the segmentation strength of SAM. It does well in proposing a practical way to adapt foundational models to a specialized field without requiring task-specific training, which aligns with zero-shot goals. The description of leveraging complementary strengths between generative and segmentation models is clear enough on a high level. The main soft spot is the complete absence of supporting details. No datasets are named, no baselines are listed, no description of the pipeline architecture or how the cues are actually used, and no evaluation protocol. The 14% Acc@0.5 improvement is stated but can't be checked. This leaves the central claim hanging. The assumption that diffusion cues will be accurate and helpful without net harm in varied RS scenes is plausible but untested in the provided text. This paper is aimed at computer vision practitioners working on remote sensing or zero-shot segmentation tasks. Someone looking for new ways to combine foundation models might get some value from the concept, but only if the full version includes the experiments and code. It shows clear thinking in the sense that it applies known techniques to a new setting without obvious contradictions. I would bring it to a reading group as a maybe, to discuss the potential of diffusion guidance in specialized imagery. I would not cite it in my own work until I see the actual results and comparisons. It deserves peer review because the application area is relevant and the idea could be useful if the numbers hold up, even though heavy revision on the experimental section would be expected.

Referee Report

1 major / 0 minor

Summary. The paper introduces DiffuSAM, a hybrid pipeline that integrates diffusion-based localization cues with state-of-the-art segmentation models such as RemoteSAM and SAM3 for zero-shot object grounding in remote sensing imagery. By combining generative diffusion models and foundational segmentation models, it aims to achieve robust and adaptive object localization in complex scenes, with reported experiments showing over a 14% increase in Acc@0.5 compared to existing state-of-the-art methods.

Significance. Should the performance gains be validated through detailed experiments, this approach could significantly impact the field of remote sensing image analysis by providing a more effective zero-shot grounding method that leverages the strengths of diffusion models for localization cues. The work highlights a promising direction for improving object detection in challenging aerial and satellite imagery without requiring task-specific training.

major comments (1)

Abstract: The central claim of achieving 'over a 14% increase in Acc@0.5' is not accompanied by any information on the datasets, baselines, implementation details of the diffusion-guided pipeline, or the evaluation metrics and protocol. This makes it impossible to evaluate the soundness of the empirical results or the complementarity of the diffusion cues with the segmentation models.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We agree that the abstract would benefit from additional context on the experimental setup to better support the reported performance improvements. We will revise the abstract in the next version of the manuscript to address this.

read point-by-point responses

Referee: Abstract: The central claim of achieving 'over a 14% increase in Acc@0.5' is not accompanied by any information on the datasets, baselines, implementation details of the diffusion-guided pipeline, or the evaluation metrics and protocol. This makes it impossible to evaluate the soundness of the empirical results or the complementarity of the diffusion cues with the segmentation models.

Authors: We acknowledge that the current abstract is concise and does not include specifics on datasets, baselines, or protocols. The full manuscript already details the remote sensing datasets used for zero-shot evaluation, the compared state-of-the-art baselines (including prior grounding methods), the diffusion pipeline implementation, and metrics such as Acc@0.5 with the exact evaluation protocol. To improve accessibility, we will expand the abstract to concisely reference these elements (e.g., key datasets and baselines) while preserving its brevity. This change will allow readers to immediately contextualize the 14% gain without altering the core claims or results. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical applied pipeline that combines diffusion-based localization cues with segmentation models such as SAM for remote-sensing object grounding. No derivation chain, equations, fitted parameters presented as predictions, or first-principles results appear in the abstract or described content. Claims rest on experimental performance gains rather than any self-definitional, self-citation load-bearing, or ansatz-smuggled steps. The work is self-contained as a practical method without internal reductions to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no description of free parameters, axioms, or invented entities; the approach is summarized at a conceptual level only.

pith-pipeline@v0.9.0 · 5417 in / 1127 out tokens · 78408 ms · 2026-05-10T05:00:01.031014+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

39 extracted references · 12 canonical work pages · 6 internal anchors

[1]

2025 , eprint=

Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition , author=. 2025 , eprint=

2025
[2]

2025 , eprint=

Qwen3 Technical Report , author=. 2025 , eprint=

2025
[3]

Proceedings of the 33rd ACM International Conference on Multimedia , pages=

Remotesam: Towards segment anything for earth observation , author=. Proceedings of the 33rd ACM International Conference on Multimedia , pages=
[4]

SAM 3: Segment Anything with Concepts

SAM 3: Segment Anything with Concepts , author=. arXiv preprint arXiv:2511.16719 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[5]

Advances in Neural Information Processing Systems , volume=

Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding , author=. Advances in Neural Information Processing Systems , volume=
[6]

IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium , pages=

Object detection and instance segmentation in remote sensing imagery based on precise mask R-CNN , author=. IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium , pages=. 2019 , organization=

2019
[7]

Falcon: A remote sensing vision-language foundation model.arXiv preprint arXiv:2503.11070, 2025

Falcon: A remote sensing vision-language foundation model , author=. arXiv preprint arXiv:2503.11070 , year=

work page arXiv
[8]

Segment Anything

Segment Anything , author=. arXiv:2304.02643 , year=

work page internal anchor Pith review arXiv
[9]

SAM 2: Segment Anything in Images and Videos

SAM 2: Segment Anything in Images and Videos , author=. arXiv preprint arXiv:2408.00714 , url=

work page internal anchor Pith review arXiv
[10]

Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Grounding dino: Marrying dino with grounded pre-training for open-set object detection , author=. arXiv preprint arXiv:2303.05499 , year=

work page Pith review arXiv
[11]

IEEE Transactions on Geoscience and Remote Sensing , year=

Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain , author=. IEEE Transactions on Geoscience and Remote Sensing , year=
[12]

2025 , eprint=

EarthMind: Leveraging Cross-Sensor Data for Advanced Earth Observation Interpretation with a Unified Multimodal LLM , author=. 2025 , eprint=

2025
[13]

A survey on object detection in optical remote sensing images , volume=

Cheng, Gong and Han, Junwei , year=. A survey on object detection in optical remote sensing images , volume=. doi:10.1016/j.isprsjprs.2016.03.014 , journal=

work page doi:10.1016/j.isprsjprs.2016.03.014 2016
[14]

IEEE Transactions on Geoscience and Remote Sensing , volume =

Exploring Models and Data for Remote Sensing Image Caption Generation , author=. IEEE Transactions on Geoscience and Remote Sensing , volume =
[15]

UCM image dataset

Nouman Ali and Bushra Zafar. UCM image dataset. 2018. doi:10.6084/m9.figshare.6085976.v2

work page doi:10.6084/m9.figshare.6085976.v2 2018
[16]

2016 International conference on computer, information and telecommunication systems (Cits) , pages=

Deep semantic understanding of high resolution remote sensing image , author=. 2016 International conference on computer, information and telecommunication systems (Cits) , pages=. 2016 , organization=

2016
[17]

reben: Refined bigearthnet dataset for remote sensing image analysis,

reben: Refined bigearthnet dataset for remote sensing image analysis , author=. arXiv preprint arXiv:2407.03653 , year=

work page arXiv
[18]

IEEE Transactions on Geoscience and Remote Sensing , volume=

Rsvg: Exploring data and models for visual grounding on remote sensing data , author=. IEEE Transactions on Geoscience and Remote Sensing , volume=. 2023 , publisher=

2023
[19]

RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing , year=

Zhang, Zilun and Zhao, Tiancheng and Guo, Yulong and Yin, Jianwei , journal=. RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing , year=
[20]

Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery? , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=
[21]

International conference on machine learning , pages=

Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation , author=. International conference on machine learning , pages=. 2022 , organization=

2022
[22]

2022 , booktitle=

Grounded Language-Image Pre-training , author=. 2022 , booktitle=

2022
[23]

Visual Instruction Tuning , author=
[24]

Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond , author=. arXiv preprint arXiv:2308.12966 , year=

work page internal anchor Pith review arXiv
[25]

International conference on machine learning , pages=

Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models , author=. International conference on machine learning , pages=. 2023 , organization=

2023
[26]

Advances in neural information processing systems , volume=

Flamingo: a visual language model for few-shot learning , author=. Advances in neural information processing systems , volume=
[27]

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Minigpt-4: Enhancing vision-language understanding with advanced large language models , author=. arXiv preprint arXiv:2304.10592 , year=

work page internal anchor Pith review arXiv
[28]

The IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=

GeoChat: Grounded Large Vision-Language Model for Remote Sensing , author=. The IEEE/CVF Conference on Computer Vision and Pattern Recognition , year=
[29]

International Journal of Applied Earth Observation and Geoinformation , volume=

GeoGPT: An assistant for understanding and processing geospatial tasks , author=. International Journal of Applied Earth Observation and Geoinformation , volume=. 2024 , publisher=

2024
[30]

ISPRS Journal of Photogrammetry and Remote Sensing , volume=

Rsgpt: A remote sensing vision language model and benchmark , author=. ISPRS Journal of Photogrammetry and Remote Sensing , volume=. 2025 , publisher=

2025
[31]

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling , author=. arXiv preprint arXiv:2412.05271 , year=

work page internal anchor Pith review arXiv
[32]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Florence-2: Advancing a unified representation for a variety of vision tasks , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
[33]

European conference on computer vision , pages=

End-to-end object detection with transformers , author=. European conference on computer vision , pages=. 2020 , organization=

2020
[34]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Rotated multi-scale interaction network for referring remote sensing image segmentation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
[35]

arXiv preprint arXiv:2407.06095 , year=

Accelerating diffusion for sar-to-optical image translation via adversarial consistency distillation , author=. arXiv preprint arXiv:2407.06095 , year=

work page arXiv
[36]

Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) , pages=

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , author=. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) , pages=
[37]

2020 , eprint=

Denoising Diffusion Probabilistic Models , author=. 2020 , eprint=

2020
[38]

and Fleiss, T

Connolly, C. and Fleiss, T. , journal=. A study of efficiency and accuracy in the transformation from RGB to CIELAB color space , year=
[39]

Graphics gems , year=

Contrast Limited Adaptive Histogram Equalization , author=. Graphics gems , year=