Abstract
Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of text-driven image transformation. SIMAT contains 6k images and 18k "transformation queries" that aim at either replacing scene elements or changing their pairwise relationships. The goal is to retrieve an image consistent with the (source image, transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to show that vanilla CLIP multimodal embeddings are not very well suited for text-driven image transformation, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage the geometric properties of pretrained universal sentence encoders (FastText, LASER and LaBSE).
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URL
https://arxiv.org/abs/2112.03162
