FashionViL: Fashion-Focused Vision-and-Language Representation Learning

Updates

😁 (01/11/2022) Updated talk and poster.
😌 (19/09/2022) Added detailed running instructions.
😊 (19/07/2022) Code released!

Abstract

Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fashion domain, existing V+L methods are inadequate as they overlook the unique characteristics of both fashion V+L data and downstream tasks. In this work, we propose a novel fashion-focused V+L representation learning framework, dubbed as FashionViL. It contains two novel fashion-specific pre-training tasks designed particularly to exploit two intrinsic attributes with fashion V+L data. First, in contrast to other domains where a V+L datum contains only a single image-text pair, there could be multiple images in the fashion domain. We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text. Second, fashion text (e.g., product description) often contains rich fine-grained concepts (attributes/noun phrases). To capitalize this, a Pseudo-Attributes Classification task is introduced to encourage the learned unimodal (visual/textual) representations of the same concept to be adjacent. Further, fashion V+L tasks uniquely include ones that do not conform to the common one-stream or two-stream architectures (e.g., text-guided image retrieval). We thus propose a flexible, versatile V+L model architecture consisting of a modality-agnostic Transformer so that it can be flexibly adapted to any downstream tasks. Extensive experiments show that our FashionViL achieves new state of the art across five downstream tasks.

Architecture

FashionViL consists of an image encoder, a text encoder and a fusion encoder. Text encoder and fusion encoder share the same parameters. We adopt six pre-training tasks for richer representation learning

Getting started

⚠️ This implementation is based on MMF (Meta AI's Modular Framework for Multimodal Research in Vision and Language). It is strongly recommended to read the MMF documentation before starting.

Dependencies installation

Install MMF from source

⚠️ The latest PyTorch officially supported by MMF is 1.9.0. Feel free to upgrade if you need some features of a later version, but the mmf_cli commands (e.g., mmf_run) will not be available.

conda create -n mmf python=3.7
conda activate mmf

git clone https://github.com/BrandonHanx/mmf.git
cd mmf
pip install --editable .
cd ..

Install extra dependencies

pip install wandb einops

(Optional) Log in W&B

export WANDB_API_KEY=YOUR_KEY

(Optional) Install lint hook if you want to make contribution

pre-commit install .

Data preparation

Download datasets

⚠️ Please note that we do not own the copyrights of any datasets we used. We can only share the pre-processed caption data here via Google Drive. Please contact the original authors to get access to the images.

Download pre-trained models

You can download our pre-trained VQVAE via Google Drive.

Organise data directory as following

⚠️ The default location of data is in ./. You can put it anywhere you like, but you need to specific its location via env.data_dir=XXX when calling MMF.

data
├── datasets
│   ├── BigFACAD
│   │   ├── images
│   │   ├── train_info.json
│   │   └── val_info.json
│   ├── Fashion200k
│   │   ├── images
│   │   ├── test_info.json
│   │   └── train_info.json
│   ├── FashionGen
│   │   ├── train
│   │   ├── train_info.json
│   │   ├── val
│   │   └── val_info.json
│   ├── FashionIQ
│   │   ├── captions
│   │   ├── images
│   │   └── image_splits
│   └── PolyvoreOutfits
│       ├── images
│       ├── ocir_disjoint_test.json
│       ├── ocir_disjoint_train.json
│       ├── ocir_nondisjoint_test.json
│       ├── ocir_nondisjoint_train.json
│       └── polyvore_item_metadata.json
└── pretrained_models
    └── vqvae_ema_pp_224_7x7_encoder_fashionall.pth

Pre-training

Pre-train FashionViL (initialised from ResNet 50 and BERT-uncased-base) on four datasets with six pretext tasks:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_pretraining_final.yaml \
model=fashionvil \
dataset=fashionall

🌟 We used 4 x RTX 3090 to train this model. We provide the pre-trained checkpoint via Google Drive in case you don't have enough GPU resources. After download, please put this model in save/fashionvil_e2e_pretrain_final/fashionvil_final.pth.

Fine-tuning

Fine-tune pre-trained FashionViL on cross-modal retrieval on FashionGen:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_contrastive.yaml \
model=fashionvil \
dataset=fashiongen

Fine-tune pre-trained FashionViL on text-guided image retrieval on FashionIQ:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_composition.yaml \
model=fashionvil \
dataset=fashioniq

Fine-tune pre-trained FashionViL on sub-category classification on FashionGen:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_classification.yaml \
model=fashionvil \
dataset=fashiongen

Fine-tune pre-trained FashionViL on outfit complementary item retrieval on PolyvoreOutfits:

python mmf_cli/run.py \
config=projects/ocir/configs/polyvore/defaults.yaml \
model=csa_net \
dataset=polyvore_ocir

Evaluation

Evaluate fine-tuned FashionViL on cross-modal retrieval on FashionGen:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_contrastive.yaml \
model=fashionvil \
dataset=fashiongen \
run_type=test \
checkpoint.resume_file=save/fashionvil_contrastive_fashiongen_e2e_pretrain_final/best.ckpt

Evaluate fine-tuned FashionViL on text-guided image retrieval on FashionIQ:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_composition.yaml \
model=fashionvil \
dataset=fashioniq \
run_type=test \
checkpoint.resume_file=save/fashionvil_composition_fashioniq_e2e_pretrain_final/best.ckpt

Evaluate fine-tuned FashionViL on sub-category classification on FashionGen:

python mmf_cli/run.py \
config=projects/fashionvil/configs/e2e_classification.yaml \
model=fashionvil \
dataset=fashiongen \
run_type=test \
checkpoint.resume_file=save/fashionvil_classification_fashiongen_e2e_sub/best.ckpt

Evaluate fine-tuned FashionViL on outfit complementary item retrieval on PolyvoreOutfits:

python mmf_cli/run.py \
config=projects/ocir/configs/polyvore/defaults.yaml \
model=csa_net \
dataset=polyvore_ocir \
run_type=test \
checkpoint.resume_file=save/polyvore_csa_disjoint_final/best.ckpt

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{han2022fashionvil,
  title={FashionViL: Fashion-Focused Vision-and-Language Representation Learning},
  author={Han, Xiao and Yu, Licheng and Zhu, Xiatian and Zhang, Li and Song, Yi-Zhe and Xiang, Tao},
  booktitle={ECCV},
  year={2022}
}

BrandonHanx / mmf