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.