Commonly Uncommon: Semantic Sparsity in Situation Recognition
Commonly Uncommon: Semantic Sparsity in Situation Recognition
Semantic sparsity is a common challenge in structured visual classification problems, when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set. This paper studies semantic sparsity in situation recognition, the task of producing structured summaries of what is …