L-verse: Bidirectional Generation Between Image and Text
LG AI Research
Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalabilty. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to make a raw RGB image into a sequence of feature vectors. To better leverage the correlation between image and text, we propose L-Verse, a novel architecture consisting of feature-augmented variational autoencoder (AugVAE) and bidirectional auto-regressive transformer (BiART) for text-to-image and image-to-text generation. Our AugVAE shows the state-of-the-art reconstruction performance on ImageNet1K validation set, along with the robustness to unseen images in the wild. Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target. L-Verse can be directly used for image-to-text or text-to-image generation tasks without any finetuning or extra object detection frameworks. In quantitative and qualitative experiments, L-Verse shows impressive results against previous methods in both image-to-text and text-to-image generation on MS-COCO Captions. We furthermore assess the scalability of L-Verse architecture on Conceptual Captions and present the initial results of bidirectional vision-language representation learning on general domain.
pip install -r requirements.txt
Place any image dataset with ImageNet-style directory structure (at least 1 subfolder) to fit the dataset into pytorch ImageFolder.
You can easily test train_vae.py with randomly generated fake data.
python train_vae.py --fake_data
For actual training, provide a config file:
python train_vae.py --configs [config_file]
Please refer to example config files in configs. You first need to train AugVAE-ML before training AugVAE-SL.
- We provide the AugVAE-SL pretrained weight on ImageNet dataset. Google Drive
This project is distributed under MIT license.
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