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Combining Natural Language with Vision represents a unique and interestingchallenge in the domain of Artificial Intelligence. The AI City Challenge Track5 for Natural Language-Based Vehicle Retrieval focuses on the problem ofcombining visual and textual information, applied to a smart-city use case. Inthis paper, we present All You Can Embed (AYCE), a modular solution tocorrelate single-vehicle tracking sequences with natural language. The mainbuilding blocks of the proposed architecture are (i) BERT to provide anembedding of the textual descriptions, (ii) a convolutional backbone along witha Transformer model to embed the visual information. For the training of theretrieval model, a variation of the Triplet Margin Loss is proposed to learn adistance measure between the visual and language embeddings. The code ispublicly available at https://github.com/cscribano/AYCE_2021.

All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers