“This Is My Unicorn, Fluffy”: Personalizing Frozen Vision-Language Representations

Large Vision & Language models pretrained on web-scale data provide representations that are invaluable for numerous V &L problems. However, it is unclear how they can be extended to reason about user-specific visual concepts in unstructured language. This problem arises in multiple domains, from personalized image retrieval to personalized interaction with smart devices. We introduce a new learning setup called Personalized Vision & Language (PerVL) with two new benchmark datasets for retrieving and segmenting user-specific (“personalized”) concepts “in the wild”. In PerVL, one should learn personalized concepts (1) independently of the downstream task (2) allowing a pretrained model to reason about them with free language, and (3) without providing personalized negative examples. We propose an architecture for solving PerVL that operates by expanding the input vocabulary of a pretrained model with new word embeddings for the personalized concepts. The model can then simply employ them as part of a sentence. We demonstrate that our approach learns personalized visual concepts from a few examples and effectively applies them in image retrieval and semantic segmentation using rich textual queries. For example the model improves MRR by 51.1% (28.4% vs 18.8%) compared to the strongest baseline. The code and benchmark are available on github under NVlabs/PALAVRA (https://github.com/NVlabs/PALAVRA ) and NVlabs/PerVLBenchmark (https://github.com/NVlabs/PerVLBenchmark ).