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A word in natural language can be polysemous, having multiple meanings, as well as synonymous, meaning the same thing as other words. Word sense induction attempts to find the senses of pol- ysemous words. Synonymy detection attempts to find when two words are interchangeable. We com- bine these tasks, first inducing word senses and then detecting similar senses to form word-sense synonym sets (synsets) in an unsupervised fashion. Given pairs of images and text with noun phrase labels, we perform synset induction to produce col- lections of underlying concepts described by one or more noun phrases. We find that considering multi- modal features from both visual and textual context yields better induced synsets than using either con- text alone. Human evaluations show that our unsu- pervised, multi-modally induced synsets are com- parable in quality to annotation-assisted ImageNet synsets, achieving about 84% of ImageNet synsets’ approval.