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Content-based image retrieval has seen astonishing progress over the pastdecade, especially for the task of retrieving images of the same object that isdepicted in the query image. This scenario is called instance or objectretrieval and requires matching fine-grained visual patterns between images.Semantics, however, do not play a crucial role. This brings rise to thequestion: Do the recent advances in instance retrieval transfer to more genericimage retrieval scenarios? To answer this question, we first provide a briefoverview of the most relevant milestones of instance retrieval. We then applythem to a semantic image retrieval task and find that they perform inferior tomuch less sophisticated and more generic methods in a setting that requiresimage understanding. Following this, we review existing approaches to closingthis so-called semantic gap by integrating prior world knowledge. We concludethat the key problem for the further advancement of semantic image retrievallies in the lack of a standardized task definition and an appropriate benchmarkdataset.

Content-based Image Retrieval and the Semantic Gap in the Deep Learning Era