  Feature similarity matching, which transfers the information of the reference
frame to the query frame, is a key component in semi-supervised video object
segmentation. If surjective matching is adopted, background distractors can
easily occur and degrade the performance. Bijective matching mechanisms try to
prevent this by restricting the amount of information being transferred to the
query frame, but have two limitations: 1) surjective matching cannot be fully
leveraged as it is transformed to bijective matching at test time; and 2)
test-time manual tuning is required for searching the optimal hyper-parameters.
To overcome these limitations while ensuring reliable information transfer, we
introduce an equalized matching mechanism. To prevent the reference frame
information from being overly referenced, the potential contribution to the
query frame is equalized by simply applying a softmax operation along with the
query. On public benchmark datasets, our proposed approach achieves a
comparable performance to state-of-the-art methods.
