Training Vision-Language Transformers from Captions
This work addresses the need for more efficient and interpretable vision-language models, reducing reliance on supervised pretraining, though it is incremental as it builds on existing methods like Masked Auto-Encoders.
The paper tackles the problem of training vision-language transformers without low-level human labels like ImageNet class predictions, showing that their model VLC outperforms the state-of-the-art ViLT on standard benchmarks and provides more interpretable visualizations.
Vision-Language Transformers can be learned without low-level human labels (e.g. class labels, bounding boxes, etc). Existing work, whether explicitly utilizing bounding boxes or patches, assumes that the visual backbone must first be trained on ImageNet class prediction before being integrated into a multimodal linguistic pipeline. We show that this is not necessary and introduce a new model Vision-Language from Captions (VLC) built on top of Masked Auto-Encoders that does not require this supervision. In fact, in a head-to-head comparison between ViLT, the current state-of-the-art patch-based vision-language transformer which is pretrained with supervised object classification, and our model, VLC, we find that our approach 1. outperforms ViLT on standard benchmarks, 2. provides more interpretable and intuitive patch visualizations, and 3. is competitive with many larger models that utilize ROIs trained on annotated bounding-boxes.