Despite significant advancements in Large Vision-Language Models (LVLMs)' capabilities, existing pixel-grounding models operate in single-image settings, limiting their ability to perform detailed, fine-grained comparisons across multiple images. Conversely, current multi-image understanding models lack pixel-level grounding. Our work addresses this gap by introducing the task of multi-image pixel-grounded reasoning alongside PRIMA, an LVLM that integrates pixel-level grounding with robust multi-image reasoning to produce contextually rich, pixel-grounded explanations. Central to PRIMA is SQuARE, a vision module that injects cross-image relational context into compact query-based visual tokens before fusing them with the language backbone. To support training and evaluation, we curate M4SEG, a new multi-image reasoning segmentation benchmark consisting of ∼744K question-answer pairs that require fine-grained visual understanding across multiple images. PRIMA outperforms state-of-the-art baselines with 7.83% and 11.25% improvements in Recall and S-IoU, respectively. Ablation studies further demonstrate the effectiveness of the proposed SQuARE module in capturing cross-image relationships.
@article{arxiv.2412.15209,
title = {PRIMA: Multi-Image Vision-Language Models for Reasoning Segmentation},
author = {Muntasir Wahed and Kiet A. Nguyen and Adheesh Sunil Juvekar and Xinzhuo Li and Xiaona Zhou and Vedant Shah and Tianjiao Yu and Pinar Yanardag and Ismini Lourentzou},
journal= {arXiv preprint arXiv:2412.15209},
year = {2025}
}