PixelRefer: A Unified Framework for Spatio-Temporal Object Referring with Arbitrary Granularity

YuqianYuan, Wenqiao Zhang, Xin Li, Shihao Wang, Kehan Li, LI WENTONG, Jun Xiao, Lei Zhang, Beng Chin Ooi

PixelRefer: A Unified Framework for Spatio-Temporal Object Referring with Arbitrary Granularity: 26 upvotes on Hugging Face Daily Papers, #13 of 36 papers on 2025-10-28. Day-by-day upvote history.

Multimodal large language models (MLLMs) have demonstrated strong general-purpose capabilities in open-world visual comprehension. However, most existing MLLMs primarily focus on holistic, scene-level understanding, often overlooking the need for fine-grained, object-centric reasoning. In this paper, we present PixelRefer, a unified region-level MLLM framework that enables advanced fine-grained understanding over user-specified regions across both images and videos. Motivated by the observation that LLM attention predominantly focuses on object-level tokens, we propose a Scale-Adaptive Object Tokenizer (SAOT) to generate compact and semantically rich object representations from free-form regions. Our analysis reveals that global visual tokens contribute mainly in early LLM layers, inspiring the design of PixelRefer-Lite, an efficient variant that employs an Object-Centric Infusion module to pre-fuse global context into object tokens. This yields a lightweight Object-Only Framework that substantially reduces computational cost while maintaining high semantic fidelity. To facilitate fine-grained instruction tuning, we curate PixelRefer-2.2M, a high-quality object-centric instruction dataset. Extensive experiments across a range of benchmarks validate that PixelRefer achieves leading performance with fewer training samples, while PixelRefer-Lite offers competitive accuracy with notable gains in efficiency.

Paper page on Hugging Face · arXiv

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