ReferTrack-Qwen3-4B

Checkpoint package associated with ReferTrack: Referring Then Tracking for Embodied Visual Tracking.

This release provides a Qwen3-4B checkpoint for ReferTrack-style embodied visual tracking, intended to align with the official paper, project page, codebase, and demo video.

Official Resources

• Paper: arXiv 2607.20061
• Project page: ReferTrack website
• Code: MedlarTea/referTrack
• Video: YouTube demo

About ReferTrack

ReferTrack is a referring-then-tracking paradigm for embodied visual tracking. The model first grounds a language-described target to an indexed image-space bounding box, then predicts tracking waypoints conditioned on that explicit grounding decision. The method further uses temporal-viewpoint-bbox indicator (TVBI) tokens to preserve target motion cues over time and improves target identification through co-training with Refer-QA data.

EVT-Bench Results

The official paper reports the following single forward-view results on EVT-Bench (Table 1), where each split is evaluated with Success Rate (SR), Tracking Rate (TR), and Collision Rate (CR):

Split	SR	TR	CR
STT	89.4	92.5	1.6
DT	73.3	81.8	7.6
AT	74.1	85.7	7.7

These numbers correspond to the official ReferTrack results reported for the Qwen3-4B single-view setting in the paper.

Files

File	Description
refertrack_qwen3_4b.pt	Model weights
model_config.json	Model structure and task settings

Usage

Load with your ReferTrack / social-RPF code:

import torch

ckpt = torch.load("refertrack_qwen3_4b.pt", map_location="cpu")
# Then load into your model according to model_config.json

Citation

If you use this checkpoint, please cite ReferTrack:

@article{ye2026refertrack,
  title={ReferTrack: Referring Then Tracking for Embodied Visual Tracking},
  author={Ye, Hanjing and Zeng, Tianle and Zhang, Jiazhao and Wang, Shaoan and Zhang, Zibo and Situ, Weixi and Zhou, Yuchen and Ling, Yonggen and Zhang, Hong},
  journal={arXiv preprint arXiv:2607.20061},
  year={2026},
  url={https://arxiv.org/abs/2607.20061}
}