MagicLens

This repo contains implementation of MagicLens. The code here uses Jax and Flax. Note that the current implementation does not yet support training. Refer to the website for dataset examples.

Abstract

We introduce MagicLens, a series of self-supervised image retrieval models that support open-ended instructions. The core thesis of MagicLens is that text instructions can enable retrieving images with richer relations beyond visual similarity. MagicLens is built on a key novel insight: image pairs that naturally occur on the same web pages contain a wide range of implicit relations (e.g., inside view of), and we can bring those implicit relations explicit by synthesizing instructions via large multimodal models (LMMs) and large language models (LLMs). Trained on 36.7M (query image, instruction, target image) triplets with rich semantic relations mined from the web, MagicLens achieves comparable or better results on eight benchmarks of various image retrieval tasks than prior state-of-the-art (SOTA) methods. Remarkably, it outperforms previous SOTA but with a 50× smaller model size on multiple benchmarks. Additional human analyses on a 1.4M-image unseen corpus further demonstrate the diversity of search intents supported by MagicLens.

Setup

conda create --name magic_lens python=3.9
conda activate magic_lens
git clone https://github.com/google-research/scenic.git
cd scenic
pip install .
pip install -r scenic/projects/baselines/clip/requirements.txt
# you may need to install corresponding GPU version of jax following https://jax.readthedocs.io/en/latest/installation.html
# e.g.,
# # CUDA 12 installation
# Note: wheels only available on linux.
# pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# # CUDA 11 installation
# Note: wheels only available on linux.
# pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

Model Download

Download model via:

cd .. # in main folder `magiclens`
# you may need to use `gcloud auth login` for access, any gmail account should work.
gsutil cp -R gs://gresearch/magiclens/models ./

Data Preparation

Please follow each dataset folder in ./data. Currently we have successfully tested FIQ and CIRCO:

Inference

python inference.py \
--model_size large \
--model_path ./models/magic_lens_clip_large.pkl \
--dataset circo

Due to the weight conversion, the performance may be slightly different:

In CIRCO

Model	map@5	map@10	map@25	map@50
Prior SOTA	26.8	27.6	30.0	31.0
Base (original)	23.1	23.8	25.8	26.7
Base (converted)	22.3	23.2	25.0	26.0
Large (original)	29.6	30.8	33.4	34.4
Large (converted)	29.5	30.8	33.2	34.3

Citing this work

Add citation details here, usually a pastable BibTeX snippet:

@inproceedings{zhang2024magiclens,
  title={MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions},
  author={Zhang, Kai and Luan, Yi and Hu, Hexiang and Lee, Kenton and Qiao, Siyuan and Chen, Wenhu and Su, Yu and Chang, Ming-Wei},
  booktitle={The Forty-first International Conference on Machine Learning (ICML)},
  year={2024},
  pages={to appear}
}

License and disclaimer

All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0

All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode

Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.

This is not an official Google product.

google-deepmind/magiclens