activity
20242026
most citedLUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images

4 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MM2026

T2VTree: User-Centered Visual Analytics for Agent-Assisted Thought-to-Video Authoring

Zhuoyun Zheng, Yu Dong, Gaorong Liang +6

Generative models have substantially expanded video generation capabilities, yet practical thought-to-video creation remains a multi-stage, multi-modal, and decision-intensive proc…

cs.AI2025

GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models

Peng Luo, Xiayin Lou, Yu Zheng +2

Geospatial modeling provides critical solutions for pressing global challenges such as sustainability and climate change. Existing large language model (LLM)-based algorithm discov…

cs.CV2024★ 4 cited

TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data

Jeremy Andrew Irvin, Emily Ruoyu Liu, Joyce Chuyi Chen +5

Large vision and language assistants have enabled new capabilities for interpreting natural images. These approaches have recently been adapted to earth observation data, but they…

cs.CV2024

Open-CD: A Comprehensive Toolbox for Change Detection

Kaiyu Li, Jiawei Jiang, Andrea Codegoni +13

We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of…

cs.CV2024★ 4 cited

LUWA Dataset: Learning Lithic Use-Wear Analysis on Microscopic Images

Jing Zhang, Irving Fang, Juexiao Zhang +7

Lithic Use-Wear Analysis (LUWA) using microscopic images is an underexplored vision-for-science research area. It seeks to distinguish the worked material, which is critical for un…