Impact of Language Guidance: A Reproducibility Study
This is an incremental reproducibility study for researchers in self-supervised learning, addressing dataset quality issues.
The study tackled the problem of verifying claims about language guidance in self-supervised learning by reproducing experiments and found that the original dataset had low-quality captions; replacing them with BLIP-2 improved performance, and a new metric was devised for evaluation.
Modern deep-learning architectures need large amounts of data to produce state-of-the-art results. Annotating such huge datasets is time-consuming, expensive, and prone to human error. Recent advances in self-supervised learning allow us to train huge models without explicit annotation. Contrastive learning is a popular paradigm in self-supervised learning. Recent works like SimCLR and CLIP rely on image augmentations or directly minimizing cross-modal loss between image and text. Banani et al. (2023) propose to use language guidance to sample view pairs. They claim that language enables better conceptual similarity, eliminating the effects of visual variability. We reproduce their experiments to verify their claims and find that their dataset, RedCaps, contains low-quality captions. We use an off-the-shelf image captioning model, BLIP-2, to replace the captions and improve performance, and we also devise a new metric to evaluate the semantic capabilities of self-supervised models based on interpretability methods.