An Image is Worth Multiple Words: Learning Object Level Concepts using
  Multi-Concept Prompt Learning
Chen Jin Ryutaro Tanno Amrutha Saseendran Tom Diethe Philip Teare
Textural Inversion, a prompt learning method, learns a singular 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 and integrating multiple object-level concepts
within one scene poses significant challenges even when embeddings for
individual concepts are attainable. This is further confirmed by our empirical
tests. To address this challenge, we introduce a framework for Multi-Concept
Prompt Learning (MCPL), where multiple new "words" are simultaneously learned
from a single sentence-image pair. To enhance the accuracy of word-concept
correlation, we propose three regularisation techniques: Attention Masking
(AttnMask) to concentrate learning on relevant areas; Prompts Contrastive Loss
(PromptCL) to separate the embeddings of different concepts; and Bind adjective
(Bind adj.) to associate new "words" with known words. We evaluate via image
generation, editing, and attention visualisation with diverse images. Extensive
quantitative comparisons demonstrate that our method can learn more
semantically disentangled concepts with enhanced word-concept correlation.
Additionally, we introduce a novel dataset and evaluation protocol tailored for
this new task of learning object-level concepts.