Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities

Abstract

Large-scale multi-modal pre-training models such as CLIP [30] and PaLI [8] exhibit strong generalization on various visual domains and tasks. However, existing image classification benchmarks often evaluate recognition on a specific domain (e.g., outdoor images) or a specific task (e.g., classifying plant species), which falls short of evaluating whether pre-trained foundational models are universal visual recognizers. To address this, we formally present the task of Open-domain Visual Entity recognitioN (Oven), where a model need to link an image onto a Wikipedia entity with respect to a text query. We construct Oven-Wiki ‡ by repurposing 14 existing datasets with all labels grounded onto one single label space: Wikipedia entities. Oven-Wiki challenges models to select among six million possible Wikipedia entities, making it a general visual recognition benchmark with the largest number of labels. Our study on state-ofthe-art pre-trained models reveals large headroom in generalizing to the massive-scale label space. We show that a PaLI-based auto-regressive visual recognition model performs surprisingly well, even on Wikipedia entities that have never been seen during fine-tuning. We also find existing pretrained models yield different strengths: while PaLI-based models obtain higher overall performance, CLIP-based models are better at recognizing tail entities.