UATTA

Official implementation for Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search [SIGIR 2026].

This repository contains the SOTA reproduction code for the paper's Pretrain-then-Adapt experiments:

• CLIP/IRRA-based UATTA on CUHK-PEDES, ICFG-PEDES, and RSTPReid.
• X-VLM/CMP-based UATTA on PAB.

Requirements

Create an Anaconda environment and install the dependencies:

conda create -n uatta python=3.9 -y
conda activate uatta

# Install a PyTorch build matching your CUDA version.
pip install torch torchvision torchaudio

pip install -r requirements.txt

Data And Checkpoints

Please follow the upstream dataset/checkpoint pages for access instructions and license terms.

Resource	Official link	Notes
UATTA paper	https://arxiv.org/abs/2604.08598	Our paper page.
IRRA/HAM checkpoint	https://github.com/sssaury/HAM	HAM project/checkpoint entry.
CLIP tokenizer vocabulary	https://github.com/openai/CLIP/blob/main/clip/bpe_simple_vocab_16e6.txt.gz	Required by IRRA_UATTA/utils/simple_tokenizer.py.
CUHK-PEDES	http://xiaotong.me/static/projects/person-search-language/dataset.html	Official project page.
ICFG-PEDES	https://github.com/zifyloo/SSAN	Official dataset/code entry.
RSTPReid	https://github.com/NjtechCVLab/RSTPReid-Dataset	Official dataset repository.
CMP/X-VLM checkpoint	https://github.com/Shuyu-XJTU/CMP	CMP/PAB official repository.
PAB dataset	https://github.com/Shuyu-XJTU/CMP	PAB dataset/code/checkpoint entry.

Arrange the IRRA-side files as follows:

IRRA_UATTA/
├── data/
│   └── bpe_simple_vocab_16e6.txt.gz
├── checkpoints/
│   └── HAM_checkpoint/
│       └── random100w_2HAMcaptions/
│           └── best0.pth
├── input/
│   └── images/
│       ├── CUHK-PEDES/
│       ├── ICFG-PEDES/
│       └── RSTPReid/

Download the CLIP tokenizer vocabulary before running the IRRA-side experiments:

cd /path/to/OpenSource_Release
mkdir -p IRRA_UATTA/data
curl -L https://github.com/openai/CLIP/raw/main/clip/bpe_simple_vocab_16e6.txt.gz \
  -o IRRA_UATTA/data/bpe_simple_vocab_16e6.txt.gz

Arrange the CMP-side files as follows:

CMP_UATTA/
├── checkpoint/
│   ├── 16m_base_model_state_step_199999.th
│   └── bert-base-uncased/
├── data/
│   └── PAB/
│       ├── annotation/
│       └── image/

SOTA Reproduction

Run each experiment separately after activating the uatta environment. The configs evaluate exactly the same epoch set as the retained SOTA logs.

CUHK-PEDES

cd /path/to/OpenSource_Release/IRRA_UATTA
CUDA_VISIBLE_DEVICES=0 python tta.py \
  --config_file tta_configs/ham_cuhk_tta/uatta_config.yaml \
  --seed 42 \
  --device cuda \
  --output_dir outputs/reproduce/cuhk_pedes \
  --eval_epochs 0,1,2,4,9,14,19 \
  > logs/cuhk_reproduce.log 2>&1

ICFG-PEDES

cd /path/to/OpenSource_Release/IRRA_UATTA
CUDA_VISIBLE_DEVICES=0 python tta.py \
  --config_file tta_configs/ham_icfg_tta/uatta_config.yaml \
  --seed 42 \
  --device cuda \
  --output_dir outputs/reproduce/icfg_pedes \
  --eval_epochs 0,1,2,4,9 \
  > logs/icfg_reproduce.log 2>&1

RSTPReid

cd /path/to/OpenSource_Release/IRRA_UATTA
CUDA_VISIBLE_DEVICES=0 python tta.py \
  --config_file tta_configs/ham_rstp_tta/uatta_config.yaml \
  --seed 42 \
  --device cuda \
  --output_dir outputs/reproduce/rstpreid \
  --eval_epochs 0,1,2,4,9,14,19,29,39,49,59 \
  > logs/rstp_reproduce.log 2>&1

PAB

cd /path/to/OpenSource_Release/CMP_UATTA
mkdir -p logs outputs/reproduce/pab
CUDA_VISIBLE_DEVICES=0 python tta.py \
  --config configs/uatta_config.yaml \
  --task pab_reproduce \
  --output_dir outputs/reproduce/pab \
  --checkpoint checkpoint/16m_base_model_state_step_199999.th \
  --seed 42 \
  --device cuda \
  --eval_epochs 0,1,2,4,9,14,19,29,39,49,59 \
  --tta \
  > logs/pab_reproduce.log 2>&1

Results

The original paper/retained SOTA metrics and the latest reproduced metrics are summarized below. Full adaptation traces are recorded in SOTA_Reproduction_Report.md.

Dataset	Source	Epoch	Rank@1	Rank@5	Rank@10	mAP	mINP
CUHK-PEDES	Paper/retained SOTA	9	70.923	86.891	91.862	63.503	47.209
CUHK-PEDES	Latest reproduction	9	70.809	86.907	91.862	63.469	47.192
ICFG-PEDES	Paper/retained SOTA	9	62.152	77.318	82.950	36.110	5.934
ICFG-PEDES	Latest reproduction	9	62.006	77.232	82.784	35.923	5.821
RSTPReid	Paper/retained SOTA	49	61.850	81.050	88.400	46.304	22.276
RSTPReid	Latest reproduction	49	61.950	81.200	88.300	46.274	22.157
PAB	Paper/retained SOTA	49	76.138	98.028	99.090	86.145	86.145
PAB	Latest reproduction	49	75.784	98.028	98.989	86.001	86.001

Small numeric differences are expected across hardware, CUDA, and PyTorch versions.

Acknowledgements

This release directly builds on code from:

• IRRA: https://github.com/anosorae/IRRA
• CMP: https://github.com/Shuyu-XJTU/CMP

See THIRD_PARTY_NOTICES.md for direct fork attribution.

We also thank the following repositories for methodological or implementation references:

• 2025-ICLR-TCR: https://github.com/XLearning-SCU/2025-ICLR-TCR
• TENT: https://github.com/DequanWang/tent
• MLLM4Text-ReID: https://github.com/WentaoTan/MLLM4Text-ReID
• HAM: https://github.com/sssaury/HAM

Citation

@conference{zhang2026pretrain,
      title={Pretrain-then-Adapt: Uncertainty-Aware Test-Time Adaptation for Text-based Person Search},
      author={Jiahao Zhang and Shaofei Huang and Yaxiong Wang and Zhedong Zheng},
      year={2026},
      booktitle={SIGIR},
      doi={https://doi.org/10.1145/3805712.3809598},
      url={https://arxiv.org/abs/2604.08598},
}

Contact

For questions, please contact Jiahao Zhang at yc57963@um.edu.mo.

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