An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning

• URL: http://arxiv.org/abs/2310.12274v2
• Date: Sat, 25 May 2024 00:01:46 GMT
• Title: An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
• Authors: Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare,
• Abstract summary: Textural Inversion learns a singular text embedding for a new "word" to represent image style and appearance. We introduce Multi-Concept Prompt Learning (MCPL), where multiple unknown "words" are simultaneously learned from a single sentence-image pair. Our approach emphasises learning solely from textual embeddings, using less than 10% of the storage space compared to others.
• Score: 8.985668637331335
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images. However, identifying multiple unknown object-level concepts within one scene remains a complex challenge. While recent methods have resorted to cropping or masking individual images to learn multiple concepts, these techniques often require prior knowledge of new concepts and are labour-intensive. To address this challenge, we introduce Multi-Concept Prompt Learning (MCPL), where multiple unknown "words" are simultaneously learned from a single sentence-image pair, without any imagery annotations. To enhance the accuracy of word-concept correlation and refine attention mask boundaries, we propose three regularisation techniques: Attention Masking, Prompts Contrastive Loss, and Bind Adjective. Extensive quantitative comparisons with both real-world categories and biomedical images demonstrate that our method can learn new semantically disentangled concepts. Our approach emphasises learning solely from textual embeddings, using less than 10% of the storage space compared to others. The project page, code, and data are available at https://astrazeneca.github.io/mcpl.github.io.

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