Referring to Any Person
Authors: name: Qing Jiang, name: Lin Wu, name: Zhaoyang Zeng, name: Tianhe Ren, name: Yuda Xiong, name: Yihao Chen, name: Qin Liu, name: Lei Zhang

Humans are undoubtedly the most important participants in computer vision,
and the ability to detect any individual given a natural language description,
a task we define as referring to any person, holds substantial practical value.
However, we find that existing models generally fail to achieve real-world
usability, and current benchmarks are limited by their focus on one-to-one
referring, that hinder progress in this area. In this work, we revisit this
task from three critical perspectives: task definition, dataset design, and
model architecture. We first identify five aspects of referable entities and
three distinctive characteristics of this task. Next, we introduce HumanRef, a
novel dataset designed to tackle these challenges and better reflect real-world
applications. From a model design perspective, we integrate a multimodal large
language model with an object detection framework, constructing a robust
referring model named RexSeek. Experimental results reveal that
state-of-the-art models, which perform well on commonly used benchmarks like
RefCOCO/+/g, struggle with HumanRef due to their inability to detect multiple
individuals. In contrast, RexSeek not only excels in human referring but also
generalizes effectively to common object referring, making it broadly
applicable across various perception tasks. Code is available at
https://github.com/IDEA-Research/RexSeek