  Vision-language foundation models such as CLIP have shown impressive
zero-shot performance on many tasks and datasets, especially thanks to their
free-text inputs. However, they struggle to handle some downstream tasks, such
as fine-grained attribute detection and localization. In this paper, we propose
a multitask fine-tuning strategy based on a positive/negative prompt
formulation to further leverage the capacities of the vision-language
foundation models. Using the CLIP architecture as baseline, we show strong
improvements on bird fine-grained attribute detection and localization tasks,
while also increasing the classification performance on the CUB200-2011
dataset. We provide source code for reproducibility purposes: it is available
at https://github.com/FactoDeepLearning/MultitaskVLFM.
