arXiv AI September 10, 2026 By Zhangding Liu, Neda Mohammadi, John E. Taylor

Knowledge-Guided Vision-Language Inference for Image-Based Urban Flood Depth Estimation

The paper introduces FloodVision, a knowledge-guided vision‑language framework that estimates urban flood depth from a single RGB image. It combines a general‑purpose vision‑language model with FloodKG, a domain knowledge base that encodes canonical object dimensions and component landmarks to promote component‑level reasoning. On 654 crowdsourced New York flood images, FloodVision cuts mean absolute error from 15.62 cm to 8.75 cm and median error from 14.35 cm to 7.75 cm, outperforming the VLM‑only baseline in 69.3 % of cases.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 6

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

arXiv:2606. 05833v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames.

By Haibo Wang, Lifu Huang

llms diffusion multimodal benchmarks

Hugging Face Trending Papers

Jun 4

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences.

llms diffusion multimodal benchmarks

arXiv Computer Vision

Sep 2

Monocular Depth Estimation from a Single Image: Progress and Opportunities

arXiv:2609.01172v1 Announce Type: new Abstract: Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruct...

By Muxin Liu, Xiaoyang Lyu, Yang-Tian Sun, Yi-Hua Huang, Ziyi Yang, Peng Dai, Xiaojuan Qi

agents computer-vision robotics benchmarks

Hugging Face Trending Papers

Jun 23

Flood Mapping from RGB imagery using a Vision Foundation Model

Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.

llms computer-vision fine-tuning benchmarks

arXiv Computer Vision

23h ago

CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding

arXiv:2609.19911v1 Announce Type: new Abstract: 3D grounding aims to localize target entities in complex scenes from natural language and plays a fundamental role in embodied perception and spatial r...

By Shuai Zhang, Hongye Hou, Qinghe Liu, Zhuoxiao Li, Dongli Wu, Jing Ou, Yuan Liu, Wufan Zhao

robotics multimodal benchmarks safety

arXiv AI

Aug 18

FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge

arXiv:2608. 15410v1 Announce Type: cross Abstract: Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offering a natural perception interface for embodied agents.

By Rajat Bhattacharjya, Yoomee Jung, Minwoo Kim, Sing-Yao Wu, Eli Bozorgzadeh, Nalini Venkatasubramanian, Nikil Dutt

llms agents computer-vision robotics multimodal benchmarks

One email a morning, machine-written

Related stories

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

Monocular Depth Estimation from a Single Image: Progress and Opportunities

Flood Mapping from RGB imagery using a Vision Foundation Model

CitySTAR: Structured and Topology-Aware Reasoning for Open-Vocabulary Urban 3D Grounding

FloodReasonBench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge