[CVPR 2026] WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval

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🔥 News

• [2026/2/23] Our paper has been accepted to CVPR 2026!
• [2026/2/27] We release our paper in the arxiv.
• [2026/2/27] We release the code.

🔎 Overview

Overview of the proposed WISER framework. (1) Wider Search. We leverage an editor to produce text and image queries for dual-path retrieval, aggregating the top-K results into a unified candidate pool. (2) Adaptive Fusion. We employ a verifier to assess the candidates with confidence scores, applying a multi-level fusion strategy for high-confidence results and triggering refinement for low-confidence ones. (3) Deeper Thinking. For uncertain retrievals, we leverage a refiner to analyze unmet modifications and then feed targeted suggestions back to the editor, iterating until a predefined limit is reached.

⚡️ Getting Started

Requirements

conda create -n wiser python=3.10
conda activate wiser
pip install -r requirements.txt

Data Preparing

Option 1: Download from Hugging Face (Recommended)

We provide the complete datasets (FashionIQ and CIRR) on Hugging Face for easy access:

🤗 Download WISER Dataset from Hugging Face

# Install huggingface_hub
pip install huggingface_hub

# Download the dataset
huggingface-cli download Physicsmile/WISER --repo-type dataset --local-dir ./WISER_data

Or using Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Physicsmile/WISER",
    repo_type="dataset",
    local_dir="./WISER_data"
)

The dataset includes:

• fashion-iq.7z: FashionIQ dataset (978MB)
• CIRR.zip: CIRR dataset (4.55GB)

Option 2: Manual Download

Follow the dataset preparation from CIReVL. After downloading and organizing the datasets, update paths and parameters in ./config/start_config_circo.json.

How to Run

Here we take CIRCO dataset as an example.

Step1: Prepare Gallery Captions

bash run_step1.sh

Step 2: Inference

bash run_step2.sh

📊 Main Results

WISER significantly outperforms previous methods across multiple benchmarks, achieving relative improvements of 45% on CIRCO (mAP@5) and 57% on CIRR (Recall@1) over existing training-free methods. Notably, it even surpasses many training-dependent methods, highlighting its superiority and generalization under diverse scenarios.

    

📄 Citation

@article{wang2026wiser,
  title={WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval},
  author={Wang, Tianyue and Qu, Leigang and Yang, Tianyu and Hao, Xiangzhao and Xu, Yifan and Guo, Haiyun and Wang, Jinqiao},
  journal={arXiv preprint arXiv:2602.23029},
  year={2026}
}