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Traditional image tagging and retrieval algorithms have limited value as aresult of being trained with heavily curated datasets. These limitations aremost evident when arbitrary search words are used that do not intersect withtraining set labels. Weak labels from user generated content (UGC) found in thewild (e.g., Google Photos, FlickR, etc.) have an almost unlimited number ofunique words in the metadata tags. Prior work on word embeddings successfullyleveraged unstructured text with large vocabularies, and our proposed methodseeks to apply similar cost functions to open source imagery. Specifically, wetrain a deep learning image tagging and retrieval system on large scale, usergenerated content (UGC) using sampling methods and joint optimization of wordembeddings. By using the Yahoo! FlickR Creative Commons (YFCC100M) dataset,such an approach builds robustness to common unstructured data issues thatinclude but are not limited to irrelevant tags, misspellings, multiplelanguages, polysemy, and tag imbalance. As a result, the final proposedalgorithm will not only yield comparable results to state of the art inconventional image tagging, but will enable new capability to train algorithmson large, scale unstructured text in the YFCC100M dataset and outperform citedwork in zero-shot capability.

Sampled Image Tagging and Retrieval Methods on User Generated Content