  In recent years, there is a growing number of pre-trained models trained on a
large corpus of data and yielding good performance on various tasks such as
classifying multimodal datasets. These models have shown good performance on
natural images but are not fully explored for scarce abstract concepts in
images. In this work, we introduce an image/text-based dataset called Greeting
Cards. Dataset (GCD) that has abstract visual concepts. In our work, we propose
to aggregate features from pretrained images and text embeddings to learn
abstract visual concepts from GCD. This allows us to learn the text-modified
image features, which combine complementary and redundant information from the
multi-modal data streams into a single, meaningful feature. Secondly, the
captions for the GCD dataset are computed with the pretrained CLIP-based image
captioning model. Finally, we also demonstrate that the proposed the dataset is
also useful for generating greeting card images using pre-trained text-to-image
generation model.
