SDGBiasBench is a large-scale benchmark suite designed to evaluate and mitigate biases in vision–language models (VLMs) when reasoning about Sustainable Development Goals (SDGs). It contains 500k expert‑involved multiple‑choice questions and 50k regression tasks, allowing assessment of both decision‑level and estimation‑level bias. Experiments show that current VLMs exhibit intrinsic SDG bias, often relying on priors rather than multimodal evidence, and the proposed CADE method significantly reduces this bias, improving accuracy and reducing mean absolute error.
By Zihang Lin, Huaiyuan Qin, Muli Yang, Hongyuan Zhu
arXiv:2609.24787v1 Announce Type: new
Abstract: Forest inventories increasingly rely on artificial intelligence (AI) models to derive forest attributes from large-scale 3D point clouds. Current model...
By Yuanwen Yue, Stefano Puliti, Damien Robert, Atakan Topalo\u{g}lu, Binbin Xiang, Maciej Wielgosz, Jan Dirk Wegner, Rasmus Astrup, Christian Rupprecht, Konrad Schindler
arXiv:2606. 06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance.
By Shiyun Xiong, Dongming Wu, Peiwen Sun, Yuang Ai, Bokang Yang, Wencheng Han, Xiao-Hui Li, Xiangyu Yue
arXiv:2606. 18661v1 Announce Type: cross Abstract: Intelligent landslide hazard interpretation is critical for disaster prevention, yet current paradigms struggle to simultaneously extract visual features and high-level geoscientific semantics, while general-purpose vision-language models (VLMs) suffer from perceptual limitations and domain hallucinations in complex geological scenarios.
By Chengfu Liu, Dongyang Hou, Junwu Xiang, Cheng Yang, Xuezhi Cui, Zeyuan Wang, Liangtian Liu, Zelang Miao
arXiv:2509. 14860v2 Announce Type: replace-cross Abstract: Image classification has traditionally relied on parameter-intensive model training, requiring large-scale annotated datasets and extensive fine tuning to achieve competitive performance.
By Wonduk Seo, Minhyeong Yu, Hyunjin An, Seunghyun Lee
arXiv:2607. 15942v1 Announce Type: cross Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.
By Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")