CVPR 2026 Highlight

Yunbei Zhang   Chengyi Cai   Feng Liu   Jihun Hamm

• Apr 10, 2026 — Code released.
• Apr 08, 2026 — Selected as CVPR 2026 Highlight!
• Apr 03, 2026 — Title changed to "Prime Once, then Reprogram Locally: An Efficient Alternative to Black-Box Service Model Adaptation".
• Feb 20, 2026 — Accepted to CVPR 2026.

AReS (Alternative efficient Reprogramming for Service models) proposes an alternative to the conventional zeroth-order optimization (ZOO) paradigm for adapting closed-box service models (APIs) to downstream tasks. Instead of costly, continuous API queries, AReS performs a single-pass interaction with the service API to prime a local pre-trained encoder, then conducts all subsequent adaptation and inference entirely locally — eliminating further API costs.

(a) Previous closed-box methods use ZOO, requiring numerous API calls during training and one per image at inference. (b) AReS performs a one-time priming to prepare a local model, enabling efficient glass-box reprogramming with no further API dependency.

• Effective on modern APIs: On GPT-4o, AReS achieves +27.8% over zero-shot, where ZOO-based methods provide little to no improvement.
• State-of-the-art accuracy: Outperforms prior methods by +2.5% (VLMs) and +15.6% (VMs) on average across 10 datasets.
• 99.99% fewer API calls: Reduces API calls from ~10⁸ to ~10³, and training time from 32+ hours to under 30 minutes.
• Cost-free inference: Once primed, all inference runs locally with zero API cost.

(a) On GPT-4o, ZOO-based methods show limited effectiveness while incurring high costs. (b, c) On CLIP ViT-B/16 (Flowers102), AReS uses only ~10³ API calls and 0.4 hours vs. ~10⁸ calls and 32+ hours for prior methods.

git clone https://github.com/yunbeizhang/AReS.git
cd AReS
conda create -n AReS python=3.10 -y
conda activate AReS
pip install -r requirements.txt

Please download the datasets provided by OPTML-Group/ILM-VP, and modify data_path in src/cfg.py to point to your data directory.

Run the full AReS pipeline (VLM setting) on Flowers102 with a single command:

bash scripts/run_example_flowers.sh

This runs both stages:

Stage 1 — Prime Once: Query CLIP ViT-B/16 once per training image, train a lightweight linear layer on the local ViT-B/16 encoder.

python src/prime_vlm.py \
    --dataset flowers102 \
    --student vitb16 \
    --mode linear \
    --criterion kl \
    --lr 1e-3 \
    --epochs 100 \
    --num_samples_per_class 16 \
    --seed 0

Stage 2 — Reprogram Locally: Train a visual prompt on the primed local model using glass-box optimization.

python src/reprogram.py \
    --dataset flowers102 \
    --model vitb16 \
    --reprogramming padding \
    --mapping blmp \
    --vlm_distilled \
    --student vitb16 \
    --mode linear \
    --criterion kl \
    --num_samples_per_class 16 \
    --seed 0

This repo is built upon the following prior works:

• BayesianLM — visual reprogramming (VR) and label mapping components.
• OPTML-Group/ILM-VP — datasets and data preparation pipeline.

We sincerely thank the authors for making their code and data publicly available.

If you find this work useful, please cite our paper:

@inproceedings{zhang2026prime,
  title={Prime Once, then Reprogram Locally: An Efficient Alternative to Black-Box Service Model Adaptation},
  author={Zhang, Yunbei and Cai, Chengyi and Liu, Feng and Hamm, Jihun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}

This project is licensed under the Apache License 2.0.