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g1g122/
MoA_Lee2025_WACV

Name: MoA_Lee2025_WACV
Availability: InStock

domain-generalization

laion/CLIP-ViT-B-16-laion2B-s34B-b88K

finetune

mit

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MoA Lee 2025 WACV — Independent Reproduction

This repository contains model checkpoints from an independent reproduction of:

Domain Generalization using Large Pretrained Models with Mixture-of-Adapters
Gyuseong Lee, Wooseok Jang, Jinhyeon Kim, Jaewoo Jung, and Seungryong Kim
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025

Paper: https://doi.org/10.1109/WACV61041.2025.00801

This repository is not affiliated with or maintained by the original authors.

Model description

The checkpoints use an OpenCLIP ViT-B/16 backbone pretrained on LAION-2B:

Base model: laion/CLIP-ViT-B-16-laion2B-s34B-b88K
Dataset: PACS
Task: Domain generalization
Framework: PyTorch

Four leave-one-domain-out checkpoints are provided. Each checkpoint corresponds to one held-out PACS target domain.

Checkpoints

Held-out target domain	Checkpoint
Art painting	`pacs/art_painting.pt`
Cartoon	`pacs/cartoon.pt`
Photo	`pacs/photo.pt`
Sketch	`pacs/sketch.pt`

Checkpoint format

Each .pt checkpoint contains:

model state
optimizer state
training step
evaluation metrics
model metadata

The checkpoints require the accompanying Engram MoA implementation for model construction and evaluation.

License and attribution

The independently implemented Engram MoA code and checkpoints are released under the MIT License.

The checkpoints are based on OpenCLIP ViT-B/16 pretrained weights. OpenCLIP and the corresponding base model remain subject to their original license and attribution requirements.

Citation

bibtex

@inproceedings{DBLP:conf/wacv/LeeJKJK25,
  author       = {Gyuseong Lee and
                  Wooseok Jang and
                  Jinhyeon Kim and
                  Jaewoo Jung and
                  Seungryong Kim},
  title        = {Domain Generalization using Large Pretrained Models with Mixture-of-Adapters},
  booktitle    = {{IEEE/CVF} Winter Conference on Applications of Computer Vision, {WACV}
                  2025, Tucson, AZ, USA, February 26 - March 6, 2025},
  pages        = {8259--8269},
  publisher    = {{IEEE}},
  year         = {2025},
  url          = {https://doi.org/10.1109/WACV61041.2025.00801},
  doi          = {10.1109/WACV61041.2025.00801},
  timestamp    = {Wed, 23 Apr 2025 11:40:59 +0200},
  biburl       = {https://dblp.org/rec/conf/wacv/LeeJKJK25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

g1g122/MoA_Lee2025_WACV