arXiv Computer Vision By Pei Fu, Yike Liu, Yue Sun, Hanmo Wang, Xiuwen Xi, Ruoceng Zhang, Shaojie Zhang, Chao Jiang, Hang Li, Bingrui Liu, Zhenbo Luo, Heng Qu, Changjian Xu, Ming Xu, Jian Luan

Xiaomi-Lens-0 Technical Report

Read the original on arXiv Computer Vision →

Xiaomi‑Lens‑0 is a 4B multimodal large language model built on Qwen3.5 that predicts region masks directly from its visual representations, eliminating the need for a separate segmentation encoder. By combining vision‑encoder patch features with visual latents and mask‑token states, the model interleaves reasoning with region‑specific latents, enabling grounded chain‑of‑thought reasoning. Across all RefCOCO, RefCOCO+, and RefCOCOg splits, Xiaomi‑Lens‑0 outperforms Qwen3.5 by an average of 5.6 points in referring comprehension and achieves the highest segmentation cIoU among compared models, while also surpassing Qwen3.5 on all perception benchmarks.

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