The paper introduces RSMEB, a unified benchmark for remote‑sensing multimodal retrieval that evaluates both cross‑modal and interleaved retrieval across 21 tasks under a single ranking protocol. It also presents VLM2GeoVec, an instruction‑conditioned single‑encoder model that embeds image, text, bounding‑box, and geo‑coordinate tokens into one sequence and achieves state‑of‑the‑art performance on region‑caption, referring‑expression, and semantic geo‑aware retrieval while remaining competitive on conventional tasks. The authors provide code, checkpoints, and data on GitHub to facilitate reproducibility.
By Emanuel S\'anchez Aimar, Gulnaz Zhambulova, Fahad Shahbaz Khan, Yonghao Xu, Michael Felsberg
arXiv:2610.33551v1 Announce Type: new
Abstract: High-resolution remote sensing (RS) imagery provides fine spatial detail over large areas, making it essential for fine-grained scene understanding. Re...
By Yuxi Li, Lu Si, Xia Liao, Yidong Shen, Zheng Wei, Jun Zhang, Xin Yuan
arXiv:2608. 12987v1 Announce Type: cross Abstract: Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly.
By Kaipeng Li, Haitao Yu, Xuanchen Zhou
arXiv:2609.05518v1 Announce Type: cross
Abstract: Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, m...
By Jiacheng Cai, Zijin Hong, Zheng Yuan, Huachi Zhou, Qinggang Zhang, Xiao Huang
The paper introduces HFCMA, a hierarchical fine-grained cross‑modal alignment framework that transforms low‑level image patches and text tokens into adaptive visual regions and textual phrases through dynamic granularity discovery. It then performs structured region‑phrase matching using an entropy‑regularized optimal transport formulation, incorporating background debiasing, textual importance weighting, and prototype consistency regularization. Experiments on Flickr30K and MS‑COCO demonstrate that this approach improves fine‑grained image‑text retrieval by providing structured local alignment information and better discrimination of subtle semantic differences.
By Pengfeng Song, Shaozhong Zhang, Pengfei Shao
Due to their strong generalizable multimodal processing and reasoning capabilities, Multimodal Large Language Models (MLLMs) have demonstrated significant potential as universal image retrievers, effectively addressing diverse real-world image retrieval tasks. Nevertheless, pioneering studies, while promising, overlook the potential of fine-grained context modeling and disentangled fine-tuning objectives in enhancing MLLMs' retrieval performance, particularly for complex tasks such as long-text-to-image retrieval, visual dialog retrieval, and composed image retrieval (CIR).