Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations, text descriptions, region proposals, and temporal change cues. However, existing multimodal embedding models and benchmarks are still largely designed and evaluated around general-purpose image-text matching, leaving unclear whether unified embedding space can support heterogeneous geospatial tasks involving spatial relationships, fine-grained semantics, and temporal changes.
arXiv:2608. 03826v1 Announce Type: cross Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations, text descriptions, region proposals, and temporal change cues.
By Jiapeng Li, Yong Li, Junjie Zhou, Fan Zhang, Yu Liu
arXiv:2505.12254v3 Announce Type: replace-cross
Abstract: Existing visual place recognition (VPR) datasets predominantly rely on vehicle-mounted imagery, offer limited multimodal diversity, and under...
By Yiwei Ou, Xiaobin Ren, Ronggui Sun, Guansong Gao, Kaiqi Zhao, Manfredo Manfredini
arXiv:2609.06668v1 Announce Type: new
Abstract: Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure an...
By Sirui Zhang, Yubing Zhou, Xunkai Li, Zekai Chen, Shumeng Li, Wang Luo, Yinlin Zhu, Yujin Gao, Rong-Hua Li
The paper introduces TIC‑Bench, a new benchmark for evaluating multimodal large language models on deeply interleaved text‑image contexts. It covers logical, temporal, and spatial association tasks, totaling 2,280 questions across eight specific types. The authors benchmarked ten state‑of‑the‑art MLLMs, finding a significant performance gap versus human experts and highlighting persistent challenges in integrating evidence across interleaved visual and textual inputs.
By Zihao Wang, Xi Xiang, Yuwen Sun, Yingyu Li, Yabo Zhang, Yihan Zeng, Fan Li, Wangmeng Zuo
GraphVerse is a new benchmark for multimodal large language models that tests visual graph reasoning by combining perception, graph-based visual inputs, and multi-step reasoning. It introduces Graph-centric Image Editing strategies to actively challenge models while preserving semantics, and offers a VGR-Score metric that evaluates reasoning quality beyond simple answer accuracy. Experiments show current models have limitations in VGR and demonstrate the effectiveness of GraphVerse for broader multimodal reasoning tasks.
By Yuanfu Sun, Yuanhang Ren, Kang Li, Chuanhao Ji, Jiaxi Li, Jiajin Liu, Ninghao Liu, Qiaoyu Tan
Efficient Remote Sensing Image-Text Retrieval via Interaction-Free Recall and Text-Guided Reranking proposes a fast-then-fine (FTF) retrieval framework that first performs interaction-free multi-granular recall to quickly match query embeddings with pre-computed gallery embeddings, then refines the top‑K candidates using a parameter‑free text‑guided cross‑modal reranking module called the Balanced Text‑guided Interaction Block (BTIB). The method introduces an inter‑ and intra‑modal loss to jointly optimize cross‑modal alignment across multi‑granular representations. Experiments on public benchmarks show that FTF achieves competitive retrieval accuracy while significantly improving retrieval efficiency compared with existing methods.
By Xi Chen, Xu Chen, Xiamgyang Jia, Wei Wang, Xu Zhang, Zhenyuan Shun
arXiv:2512.02697v4 Announce Type: replace
Abstract: Cross-view geo-localization infers a location by retrieving geo-tagged reference images matching a query image. However, the traditional satellite-...
By Zixuan Song, Jing Zhang, Di Wang, Zhiming Luo, Wenbin Liu, Haonan Guo, En Wang, Bo Du, Liangpei Zhang
arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.
By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang
arXiv:2506. 02568v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated substantial efficacy in advancing graph-structured data analysis.
By Dongzhe Fan, Yi Fang, Jiajin Liu, Djellel Difallah, Qiaoyu Tan
arXiv:2605.17949v2 Announce Type: replace
Abstract: Remote sensing vision-language models (RS-VLMs) commonly employ a pretrained vision encoder and a projection module to map image features into the...
By Xiao Yang, Ronghao Fu, Zhiwen Lin, Zhuoran Duan, Lang Sun, Jiaqi Liu, Jiashun Zhu, Jiasen Hu, Xu Na, Bo Yang
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