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Xinlei Chen

9 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6
  • last author3

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.RO2
  • cs.AI1
  • cs.CL1
  • cs.LG1
same name
  • Xinlei Chen — 25 papers, h 39
  • Xinlei Chen — 7 papers
  • Xinlei Chen — 6 papers, h 8
  • Xinlei Chen — 6 papers
  • Xinlei Chen — 4 papers
  • Xinlei Chen — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

PhysFire-WM: A Physics-Informed World Model for Emulating Fire Spread Dynamics

Nan Zhou, Huandong Wang, Jiahao Li +4

Fine-grained fire prediction plays a crucial role in emergency response. Infrared images and fire masks provide complementary thermal and boundary information, yet current methods…

cs.CV2025

FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting

Nan Zhou, Huandong Wang, Jiahao Li +5

Fine-grained wildfire spread prediction is crucial for enhancing emergency response efficacy and decision-making precision. However, existing research predominantly focuses on coar…

cs.CV2025

UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces

Baining Zhao, Jianjie Fang, Zichao Dai +8

Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban 3D space remain to be explored. We introduce a ben…

cs.CV2025

Open3D-VQA: A Benchmark for Comprehensive Spatial Reasoning with Multimodal Large Language Model in Open Space

Weichen Zhang, Zile Zhou, Xin Zeng +7

Spatial reasoning is a fundamental capability of multimodal large language models (MLLMs), yet their performance in open aerial environments remains underexplored. In this work, we…

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