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

arXiv: https://arxiv.org/abs/2112.04323 · HTML: https://mlsanity.com/paper/2112.04323/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 13 of 13)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Cross-Batch Memory for Embedding Learning](https://mlsanity.com/paper/1912.06798/) (2019)
- [The 2021 Image Similarity Dataset and Challenge](https://mlsanity.com/paper/2106.09672/) (2021)
- [Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval](https://mlsanity.com/paper/2003.11211/) (2020)
- [Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset](https://mlsanity.com/paper/1906.04087/) (2019)

## Built on by (most cited first, 23 of 23)

- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision](https://mlsanity.com/paper/2211.13090/) (2022)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [Results and findings of the 2021 Image Similarity Challenge](https://mlsanity.com/paper/2202.04007/) (2022)
- [A Benchmark and Asymmetrical-Similarity Learning for Practical Image Copy Detection](https://mlsanity.com/paper/2205.12358/) (2022)
- [The 2023 Video Similarity Dataset and Challenge](https://mlsanity.com/paper/2306.09489/) (2023)
- [TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation](https://mlsanity.com/paper/2411.04709/) (2024)
- [Active Image Indexing](https://mlsanity.com/paper/2210.10620/) (2022)
- [WorldVQA: Measuring Atomic World Knowledge in Multimodal Large Language Models](https://mlsanity.com/paper/2602.02537/) (2026)
- [AnyPattern: Towards In-context Image Copy Detection](https://mlsanity.com/paper/2404.13788/) (2024)
- [Self-Supervised Representation Learning for Adversarial Attack Detection](https://mlsanity.com/paper/2407.04382/) (2024)
- [Image Copy Detection for Diffusion Models](https://mlsanity.com/paper/2409.19952/) (2024)
- [A Survey of LLM × DATA](https://mlsanity.com/paper/2505.18458/) (2025)
- [PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models](https://mlsanity.com/paper/2607.24957/) (2026)
- [Feature-compatible Progressive Learning for Video Copy Detection](https://mlsanity.com/paper/2304.10305/) (2023)
- [3rd Place Solution to Meta AI Video Similarity Challenge](https://mlsanity.com/paper/2304.11964/) (2023)
- [The Effects of Short Video-Sharing Services on Video Copy Detection](https://mlsanity.com/paper/2403.18158/) (2024)
- [Relational Self-supervised Distillation with Compact Descriptors for Image Copy Detection](https://mlsanity.com/paper/2405.17928/) (2024)
- [Origin Identification for Text-Guided Image-to-Image Diffusion Models](https://mlsanity.com/paper/2501.02376/) (2025)
- [Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization](https://mlsanity.com/paper/2309.06877/) (2023)
- [Training CLIP models on Data from Scientific Papers](https://mlsanity.com/paper/2311.04711/) (2023)
- [Tracing Copied Pixels and Regularizing Patch Affinity in Copy Detection](https://mlsanity.com/paper/2602.17484/) (2026)
- [HQ-JEPA: Hybrid Quantum Joint-Embedding Predictive Architecture for Cross-Modal Remote Sensing Representation Learning](https://mlsanity.com/paper/2605.31068/) (2026)

## Abstract (arXiv)

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: urlhttps://github.com/lyakaap/ISC21-Descriptor-Track-1st