Cross-Batch Memory for Embedding Learning

CVPR 2020 Xun WangHaozhi ZhangWeilin HuangMatthew R. Scott

Mining informative negative instances are of central importance to deep metric learning (DML), however this task is intrinsically limited by mini-batch training, where only a mini-batch of instances is accessible at each iteration. In this paper, we identify a "slow drift" phenomena by observing that the embedding features drift exceptionally slow even as the model parameters are updating throughout the training process... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Retrieval In-Shop Cross-Batch Memory [email protected] 91.3 # 2
Image Retrieval SOP Cross-Batch Memory [email protected] 80.6 # 3

Methods used in the Paper


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