Papers with V&L pre-training
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models (2021.emnlp-main)
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| Challenge: | Existing methods for creating vision-and-language models involve structural modifications and V&L pre-training. |
| Approach: | They propose to extend a language model through structural modifications and V&L pre-training to make it inherit the capability of natural language understanding from the original language model. |
| Outcome: | The proposed method improves performance of vision-and-language models by extending pre-trained models with the same pre-training. |
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions (2021.naacl-main)
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| Challenge: | Existing models require large amounts of image-caption data for pre-training . existing models require expensive data collection and curation . |
| Approach: | They propose to conduct "mask-and-predict" pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. |
| Outcome: | The proposed approach achieves performance close to a model pre-trained with aligned data, on four English benchmarks. |
LightVLP: A Lightweight Vision-Language Pre-training via Gated Interactive Masked AutoEncoders (2024.lrec-main)
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| Challenge: | Existing vision-language pre-training models use multi-modal encoders to encode image and text, causing noisy training corpora. |
| Approach: | They propose a vision-language pre-training framework with two autoencoders for efficient training . they propose masked tokens and a gated interaction mechanism to cope with noise . |
| Outcome: | The proposed model achieves 2.2% R@1 gains on COCO Text Retrieval and 1.1% on refCOCO+ on six datasets. |