GeoBiaset is a new benchmark that tests how world-level image geolocalization models react to the apparent race of a person inserted into the image foreground. The dataset contains 9,541 images, each paired with counterfactual edits across seven races while keeping the background and ground-truth coordinates unchanged. Experiments show that demographic perturbations can cause a 16–47% drop in continent-level accuracy, and that models shift their focus from geographic landmarks to facial features when landmarks are absent.
By Niccol\`o Niccoli, Silvia Dani, Marco Mistretta, Andrew D. Bagdanov, Marco Bertini, Lorenzo Seidenari
arXiv:2508. 05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings.
By Yufei Gao, Jiaying Fei, Nuo Chen, Ruirui Chen, Guohang Yan, Yunshi Lan, Botian Shi
Rapid advancements in vision-language models have propelled Referring Remote Sensing Image Segmentation (RRSIS) to the forefront of Earth observation. However, practical deployments suffer severe perf...
The Cultural Moment Benchmark (CMB) evaluates video cultural reasoning in Southeast Asia by testing three distinct abilities: naming a cultural concept, visually recognizing it in a video, and locating its sub‑events in time. It contains 306 expert‑curated concepts from seven countries across five categories, with each concept assessed through three stages that use semantic‑similarity distractors, unlabeled video moments, and free‑form temporal localization. Experiments on six vision‑language models reveal varied failure modes, limited cascading between abilities, and differing impacts of audio and subtitles, while a human study shows even experts struggle with concepts from neighboring countries.
By Burak Satar, Zhixin Ma, Cheng Yu-Tong, Huy Hoang Tran, Phuong Anh Nguyen, Chong-Wah Ngo
arXiv:2610.07269v1 Announce Type: cross
Abstract: Cross-view geo-localization is commonly solved as an image retrieval problem, matching a ground-level image against a database of satellite tiles thr...
By Ayesh Abu Lehyeh, Jay Hwasung Jung, Safwan Wshah
The paper introduces VPRef, the first cross‑domain benchmark for Referring Remote Sensing Image Segmentation, containing 46,972 language‑image‑annotation triplets with a three‑tier linguistic hierarchy. It proposes a parameter‑efficient adaptation method based on the Segment Anything Model and Low‑Rank Adaptation, using pseudo‑label self‑training for visual drift and random multi‑granularity prompt mixing for textual drift. Experiments show the approach improves cross‑domain segmentation while altering only 1.08 % of the base model’s parameters, offering a strong baseline for future research.
By Quanwei Liu, Tao Huang, Jiaqi Yang, Wei Xiang
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
By Daniela Baiamonte, Elena Fano, Matteo Gabburo, Stefano Simonazzi, Leonardo Rigutini, Andrea Zugarini
arXiv:2605. 16713v2 Announce Type: replace-cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behind, and between.
By Renjie Gu, Kaichen Zhou, Yan Luo, Mengyu Wang
arXiv:2602.01161v2 Announce Type: replace
Abstract: The global deployment of large language models (LLMs) has raised concerns about cultural misalignment, yet the linguistic properties of fine-tuning...
By Reem I. Masoud, Chen Feng, Shunta Asano, Saied Alshahrani, Philip Colin Treleaven, Miguel R. D. Rodrigues
SEA-CLIP-Tiny is a compact multilingual text‑vision embedding model designed for Southeast Asian languages, containing fewer than 50 million parameters. It adapts a CLIP‑KD framework with region‑specific data curation and multilingual teacher guidance. Across seven languages, it outperforms other student models, achieving R@1 = 12.9%, R@5 = 31.5%, and R@10 = 42.2%, and surpasses MobileCLIP2 by 12.1 points in R@10 while using 38.4% fewer parameters and lower CPU latency.
By Puja Ahmad Habibi, Faiz Assabil Firdaus, Ashvanth S, Ekapol Chuangsuwanich, Pume Tuchinda, Peerat Limkonchotiwat
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...
By Yutong Hu, Jinhui Chen, Chaoqiang Xu, Yuan Kou, Sili Zhou, Shaocheng Yan, Pengcheng Shi, Qingwu Hu, Jiayuan Li
The paper introduces FindIt, the first comprehensive benchmark for evaluating the promptable localization abilities of generalist multimodal large language models (MLLMs). It covers four core task categories—object detection, referring expression detection, instance-level detection, and video-based detection—and provides a unified framework that standardizes inputs, enforces parsable bounding box outputs, and defines transparent evaluation protocols. Using this benchmark, the authors assess a range of open-source and proprietary MLLMs, revealing both their strengths and limitations, particularly their sensitivity to formatting constraints and difficulty generalizing to minor variations.
By Eshika Khandelwal, Jingjing Pan, Mingfang Zhang, Quan Kong, Lorenzo Garattoni, Hilde Kuehne