# Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime Source: [Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:15:37.285Z Authors: Chuhan Zhang, Antoine Miech, Jiajun Shen, Jean-Baptiste Alayrac, Pauline Luc Published: 2023-05-03T17:42:54.000Z ## Abstract Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks. While humans are known to efficiently learn new tasks from a few examples, deep learning models struggle with adaptation from few examples. In this work, we look into task adaptation in the low-data regime, and provide a thorough study of the existing adaptation methods for generative Visual Language Models. And we show important benefits of self-labelling, i.e. using the model's own predictions to self-improve when having access to a larger number of unlabelled images of the same distribution. Our study demonstrates significant gains using our proposed task adaptation pipeline across a wide range of visual language tasks such as visual classification (ImageNet), visual captioning (COCO), detailed visual captioning (Localised Narratives) and visual question answering (VQAv2). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()