SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models

• URL: http://arxiv.org/abs/2605.10576v1
• Date: Mon, 11 May 2026 13:48:57 GMT
• Title: SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models
• Abstract summary: Low-level visual perception underpins reliable remote sensing (RS) image analysis.<n>Current image quality assessment (IQA) methods output uninterpretable scalar scores rather than characterizing physics-driven RS degradations.<n>We propose SenseBench, the first dedicated diagnostic benchmark for RS low-level visual perception and description.
• Score: 9.606452752502708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpretable scalar scores rather than characterizing physics-driven RS degradations, deviating markedly from the diagnostic needs of RS experts. While Vision-Language Models (VLMs) present a compelling alternative by delivering language-grounded IQA, their visual priors are heavily biased toward ground-level natural images. Consequently, whether VLMs can overcome this domain gap to perceive and articulate RS artifacts remains insufficiently studied. To bridge this gap, we propose \textbf{SenseBench}, the first dedicated diagnostic benchmark for RS low-level visual perception and description. Driven by a physics-based hierarchical taxonomy that unifies both non-reference and reference-based paradigms, SenseBench features over 10K meticulously curated instances across 6 major and 22 fine-grained RS degradation categories. Specifically, two complementary protocols are designed for evaluation: objective low-level visual \textit{perception} and subjective diagnostic \textit{description}. Comprehensive evaluation of 29 state-of-the-art VLMs reveals not only skewed domain priors and multi-distortion collapse, but also \textit{fluency illusion} and a \textit{perception-description inversion} effect. We hope SenseBench provides a robust evaluation testbed and high-quality diagnostic data to advance the development of VLMs in RS low-level perception. Code and datasets are available \href{https://github.com/Zhong-Chenchen/SenseBench}{\textcolor{blue}{here}}.

Related papers

• ExACT: Exemplar-Driven Calibrated Refinement for Training-Free Visual Grounding in Remote Sensing Images [51.53960096934913]
  Remote sensing visual grounding (RSVG) aims to locate specific objects in high-resolution RS imagery using free-form natural language descriptions.<n>Recent advances in multimodal large language models (MLLMs) show great potential for such open-vocabulary RSVG.<n>We propose Exemplar-driven Calibrated Refinement (ExACT), a training-free framework driven by a one-shot visual prompting mechanism.
  arXiv  Detail & Related papers  (2026-06-27T13:50:39Z)
• Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis [63.49939245944781]
  We introduce FG-BMK, a comprehensive fine-grained evaluation benchmark containing 1.01 million questions and 0.28 million images.<n>We find that current LVLMs remain inadequate fine-grained recognizers, with failures arising from intertwined bottlenecks in visual representations, semantic grounding, modality alignment, and category-level knowledge.
  arXiv  Detail & Related papers  (2026-06-17T13:23:26Z)
• Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback [51.08692072066352]
  Text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures.<n>Structured Defect Grounding casts T2I diagnosis as structured set prediction by modeling each defect as a location, type, reason, importance.<n>Our detector outperforms leading proprietary VLMs on structured defect grounding.
  arXiv  Detail & Related papers  (2026-06-04T13:03:16Z)
• UltraVR: A Diagnostic Ultra-Resolution Image-VQA Benchmark for Evidence-Grounded Reasoning [32.37070955496229]
  We introduce UltraVR, a diagnostic benchmark for evidence-grounded visual reasoning over ultra-resolution images.<n>Each instance includes a structured ground-truth chain of thought with step-level questions, intermediate answers, and reasoning labels.<n>We show that current models remain far from reliable on ultra-resolution reasoning.
  arXiv  Detail & Related papers  (2026-06-04T01:51:16Z)
• Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline [52.65099689153431]
  Open-vocabulary remote sensing image segmentation (OVRSIS) remains underexplored due to fragmented datasets, limited training diversity, and the lack of evaluation benchmarks.<n>We propose textitOVRSISBenchV2, a large-scale and application-oriented benchmark for OVRSIS.<n>Our results highlight the importance of realistic benchmark design and the effectiveness of perturbation-based transfer for OVRSIS.
  arXiv  Detail & Related papers  (2026-04-17T02:49:46Z)
• Visual Self-Refine: A Pixel-Guided Paradigm for Accurate Chart Parsing [76.2602505940467]
  Existing models often struggle with visually dense charts, leading to errors like data omission, misalignment, and hallucination.<n>Inspired by the human strategy of using a finger as a visual anchor'' to ensure accuracy when reading complex charts, we propose a new paradigm named Visual Self-Refine (VSR)<n>The core idea of VSR is to enable a model to generate pixel-level localization outputs, visualize them, and then feed these visualizations back to itself, allowing it to intuitively inspect and correct its own potential visual perception errors.
  arXiv  Detail & Related papers  (2026-02-18T13:40:53Z)
• Text Before Vision: Staged Knowledge Injection Matters for Agentic RLVR in Ultra-High-Resolution Remote Sensing Understanding [78.26501371437013]
  Multimodal reasoning for ultra-high-resolution (UHR) remote sensing (RS) is usually bottlenecked by visual evidence acquisition.<n>We find that standard reinforcement learning struggles to navigate these vast visual spaces without structured domain priors.<n>We propose a staged knowledge injection recipe: (1) cold-starting with scalable, knowledge-graph-verified Earth-science text QA to instill reasoning structures; and (2) "pre-warming" on the same hard UHR image-text examples during SFT to stabilize and amplify subsequent tool-based RL.
  arXiv  Detail & Related papers  (2026-02-15T16:40:33Z)
• Beyond Accuracy: Evaluating Grounded Visual Evidence in Thinking with Images [34.324634481264034]
  We propose ViEBench, a process-verifiable benchmark designed to evaluate faithful visual reasoning.<n>Comprising 200 multi-scenario high-resolution images with expert-annotated visual evidence, ViEBench categorizes tasks by difficulty into perception and reasoning dimensions.<n>Our experiments yield several interesting observations: (1) VLMs can sometimes produce correct final answers despite grounding on irrelevant regions, and (2) they may successfully locate the correct evidence but still fail to utilize it to reach accurate conclusions.
  arXiv  Detail & Related papers  (2026-01-14T07:25:15Z)
• Route, Retrieve, Reflect, Repair: Self-Improving Agentic Framework for Visual Detection and Linguistic Reasoning in Medical Imaging [3.6136448489318695]
  We propose R4, an agentic framework that decomposes medical imaging into four coordinated agents.<n>R4 consistently boosts LLM-as-a-Judge scores by roughly +1.7-+2.5 points and mAP50 by +2.5-+3.5 absolute points over strong single-VLM baselines.
  arXiv  Detail & Related papers  (2026-01-13T03:44:06Z)
• Investigate the Low-level Visual Perception in Vision-Language based Image Quality Assessment [7.969076042774561]
  We introduce a low-level distortion perception task that requires models to classify specific distortion types.<n>Our analysis shows that although MLLMs are structurally capable of representing such distortions, they tend to overfit training templates.<n>We show that improving the alignment of the vision encoder dramatically enhances distortion recognition accuracy, increasing it from 14.92% to 84.43%.
  arXiv  Detail & Related papers  (2025-12-10T12:06:47Z)
• XBench: A Comprehensive Benchmark for Visual-Language Explanations in Chest Radiography [6.447908430647854]
  We present the first systematic benchmark for evaluating cross-modal interpretability in chest X-rays.<n>We generate visual explanations using cross-attention and similarity-based localization maps.<n>We quantitatively assess their alignment with radiologist-annotated regions across multiple pathologies.
  arXiv  Detail & Related papers  (2025-10-22T13:52:19Z)
• BYO-Eval: Build Your Own Dataset for Fine-Grained Visual Assessment of Multimodal Language Models [2.526146573337397]
  We propose a new evaluation methodology, inspired by ophthalmologic diagnostics.<n>We use procedural generation of synthetic images to obtain control over visual attributes.<n>This diagnostic allows systematic stress testing and fine-grained failure analysis.
  arXiv  Detail & Related papers  (2025-06-05T12:43:10Z)
• TokBench: Evaluating Your Visual Tokenizer before Visual Generation [75.38270351179018]
  We analyze text and face reconstruction quality across various scales for different image tokenizers and VAEs.<n>Our results show modern visual tokenizers still struggle to preserve fine-grained features, especially at smaller scales.
  arXiv  Detail & Related papers  (2025-05-23T17:52:16Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)

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.