Given the rise of multimedia content, human translators increasingly focus on culturally adapting not only words but also other modalities such as images to convey the same meaning. While several applications stand to benefit from this, machine translation systems remain confined to dealing with language in speech and text. In this work, we introduce a new task of translating images to make them culturally relevant. First, we build three pipelines comprising state-of-the-art generative models to do the task. Next, we build a two-part evaluation dataset – (i) concept: comprising 600 images that are cross-culturally coherent, focusing on a single concept per image; and (ii) application: comprising 100 images curated from real-world applications. We conduct a multi-faceted human evaluation of translated images to assess for cultural relevance and meaning preservation. We find that as of today, image-editing models fail at this task, but can be improved by leveraging LLMs and retrievers in the loop. Best pipelines can only translate 5% of images for some countries in the easier concept dataset and no translation is successful for some countries in the application dataset, highlighting the challenging nature of the task. Our project webpage is here: https://machine-transcreation.github.io/image-transcreation and our code, data and model outputs can be found here: https://github.com/simran-khanuja/image-transcreation.

Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis You shall know a word by the company it keeps (Harris, 1954), whereas modern prediction- based neural network embeddings rely on de- sign choices and hyperparameter optimization. Word embeddings like Word2Vec, GloVe etc. well capture the contextuality and real-world analogies but contemporary convolution-based image embeddings such as VGGNet, AlexNet, etc. do not capture contextual knowledge. The popular king-queen analogy does not hold true for most commonly used vision embeddings. In this paper, we introduce a pre-trained joint embedding (JE), named IMAGINATOR, trained on 21K distinct image objects. JE is a way to encode multimodal data into a vec- tor space where the text modality serves as the grounding key, which the complementary modality (in this case, the image) is anchored with. IMAGINATOR encapsulates three in- dividual representations: (i) object-object co- location, (ii) word-object co-location, and (iii) word-object correlation. These three ways cap- ture complementary aspects of the two modal- ities which are further combined to obtain the final object-word JEs. Generated JEs are intrinsically evaluated to assess how well they capture the contextual- ity and real-world analogies. We also evalu- ate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Im- age2Tweet, and (iii) text-based image retrieval. IMAGINATOR establishes a new standard on the aforementioned downstream tasks by out- performing the current SoTA on all the selected tasks. The code is available at https:// github.com/varunakk/IMAGINATOR.