arXiv Computer Vision
Benchmarking Fine-Grained Visual Reasoning for Remote Sensing in Urban Management
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 23
A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection
arXiv:2602.01163v2 Announce Type: replace Abstract: Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, tempora...
arXiv Computer Vision
Oct 21
LADBench: A Benchmark for Logical Fault Detection in Images
LADBench is a new benchmark comprising over 1,000 synthetic images that contain logical anomalies across four domains—Residential, Urban, Collaborative, and Nature. It introduces a Tiered Prompting Protocol that progressively discloses information to test how much explicit help a model needs to locate and reason about these faults. Evaluation of leading foundation models shows that even the best only reaches 70.11% accuracy, indicating that implicit logical fault detection remains largely unsolved.
arXiv Computer Vision
4d ago
Drone-PUR: From Visual Clue Discovery to Understanding and Reasoning in UAV Scenes
Drone-PUR is a new dataset and benchmark for vision‑language models that focuses on visual clue discovery, interpretation, and integration in UAV scenes. It contains 11 tasks with 175k training and 20k test samples across single images, image sequences, cross‑modal images, and multiple viewpoints, all of which are human‑reviewed. Evaluation of 22 general‑purpose VLMs shows persistent gaps in spatial judgment and cross‑modal/cross‑view reasoning, while fine‑tuning on Drone‑PUR improves accuracy by up to 18 percentage points.
arXiv AI
Sep 1
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
arXiv:2506.09557v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorat...
arXiv AI
Aug 12
Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
arXiv:2608. 10954v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions.
arXiv Computer Vision
Oct 15
Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework
arXiv:2603.08491v3 Announce Type: replace Abstract: Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery, which is crucial for pedestrian navigati...