Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection

Shuhei Yokoo

Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection

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arxiv 2112.04323 v1 pith:XF232NCC submitted 2021-12-08 cs.CV

Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection

Shuhei Yokoo This is my paper

classification cs.CV

keywords copydetectionimagecnnscontrastiveembeddinglargelearning

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Copy detection, which is a task to determine whether an image is a modified copy of any image in a database, is an unsolved problem. Thus, we addressed copy detection by training convolutional neural networks (CNNs) with contrastive learning. Training with a large memory-bank and hard data augmentation enables the CNNs to obtain more discriminative representation. Our proposed negative embedding subtraction further boosts the copy detection accuracy. Using our methods, we achieved 1st place in the Facebook AI Image Similarity Challenge: Descriptor Track. Our code is publicly available here: \url{https://github.com/lyakaap/ISC21-Descriptor-Track-1st}

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