arXiv Computer Vision
UHR-Micro: Diagnosing and Mitigating the Resolution Illusion in Earth Observation VLMs
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arXiv Computer Vision
Sep 17
Understanding Dynamic Scenes at Gigapixel Scale: Wide-Area Spatio-Temporal Perception from UAVs
The paper introduces the Wide-area Spatio-temporal Scene Understanding (WSTU) problem, which demands simultaneous wide-area coverage, per-target resolution, and temporal continuity—capabilities lacking in existing datasets. To address this, the authors present HARD, an ultra‑high‑resolution (12768×9564) UAV dataset annotated for object detection, multi‑object tracking, and scene‑level visual question answering. They also propose a latency‑aware metric, streaming‑HOTA (s‑HOTA), and show through baseline experiments that high resolution and processing latency significantly impact detection, tracking, and VQA performance, revealing gaps in current methods for WSTU.
arXiv Computer Vision
Sep 3
Beyond Appearance: Can Multimodal Large Language Models Exploit Vertical Structure for Remote Sensing Natural Scene Understanding?
The paper introduces VertiCue-Bench, a diagnostic benchmark designed to test whether multimodal large language models (MLLMs) can perceive, ground, and utilize vertical structure information in remote-sensing natural scenes. It presents a three-stage framework—Perception, Grounding, Utilization—and a Representation Intervention Spectrum across various modalities to evaluate ten state-of-the-art models. The study finds a significant Vertical Structure Utilization Gap: while models show some geometric perception, they struggle to accurately link vertical evidence to spatial entities and incorporate it into semantic decisions.
arXiv Computer Vision
Sep 22
Pay More Attention To Text In High-Resolution MLLMs
The paper introduces EviSpec, a training‑free compiler that generates complementary evidence specifications to improve high‑resolution multimodal large language models (MLLMs). By explicitly guiding visual search with structured evidence specifications, EviSpec achieves significant relative gains—up to 14.8% over random evidence—across five MLLMs and three benchmarks, and also sets new state‑of‑the‑art results on VQA and hallucination‑focused tasks.
arXiv Computer Vision
3d ago
SalArt-VQA: Diagnosing Whether VLMs Understand Salient Artifacts in Generated Images
SalArt-VQA is a diagnostic benchmark that tests whether vision‑language models can understand salient artifacts in AI‑generated images. It includes 950 images and 3,681 multiple‑choice questions that assess artifact presence, semantic localization, spatial grounding, and evidence‑grounded defect identification. The benchmark reveals that high image‑level detection accuracy can mask failures in grounded understanding, showing a trade‑off between sensitivity and calibration.
arXiv Computer Vision
Oct 2
Beyond Localization: A Comprehensive Benchmark of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
The paper introduces PCSR-Bench, a benchmark of 84,373 question‑answer pairs derived from 2,600 omnidirectional images across 26 indoor environments, designed to evaluate perspective‑conditioned spatial reasoning (PCSR) in multimodal large language models (MLLMs). It reports a significant perception–reasoning gap, with accuracy dropping from 57.59% on limited field‑of‑view reasoning to as low as 0.64% on open‑ended compositional directional chains. An RL‑based diagnostic study on a 7B‑scale model shows that reward shaping can improve performance to 60.06% on a controlled task, indicating partial plasticity of PCSR capabilities.
arXiv AI
Jun 2
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration
arXiv:2605. 00310v2 Announce Type: replace-cross Abstract: Super-resolution (SR) techniques have made major advances in reconstructing high-resolution images from low-resolution inputs.