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We propose to learn word embeddings from visual co-occurrences. Two wordsco-occur visually if both words apply to the same image or image region.Specifically, we extract four types of visual co-occurrences between object andattribute words from large-scale, textually-annotated visual databases likeVisualGenome and ImageNet. We then train a multi-task log-bilinear model thatcompactly encodes word "meanings" represented by each co-occurrence type into asingle visual word-vector. Through unsupervised clustering, supervisedpartitioning, and a zero-shot-like generalization analysis we show that ourword embeddings complement text-only embeddings like GloVe by betterrepresenting similarities and differences between visual concepts that aredifficult to obtain from text corpora alone. We further evaluate our embeddingson five downstream applications, four of which are vision-language tasks.Augmenting GloVe with our embeddings yields gains on all tasks. We also findthat random embeddings perform comparably to learned embeddings on allsupervised vision-language tasks, contrary to conventional wisdom.

ViCo: Word Embeddings from Visual Co-occurrences