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

• URL: http://arxiv.org/abs/2302.11154v2
• Date: Fri, 24 Feb 2023 00:50:25 GMT
• Title: Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
• Authors: Hexiang Hu, Yi Luan, Yang Chen, Urvashi Khandelwal, Mandar Joshi, Kenton Lee, Kristina Toutanova, Ming-Wei Chang
• Abstract summary: We present OVEN-Wiki, where a model need to link an image onto a Wikipedia entity with respect to a text query. 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. While PaLI-based models obtain higher overall performance, CLIP-based models are better at recognizing tail entities.
• Score: 54.26896306906937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale multi-modal pre-training models such as CLIP and PaLI 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 re-purposing 14 existing datasets with all labels grounded onto one single label space: Wikipedia entities. OVEN 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-of-the-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.

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