# Copy Detection

11 papers with code • 1 benchmarks • 1 datasets

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## Libraries

Use these libraries to find Copy Detection models and implementations
2 papers
2,730

# Emerging Properties in Self-Supervised Vision Transformers

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).

16

# LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers

This paper considers a learnable approach for comparing and aligning videos.

1

# Video Re-localization

We first exploit and reorganize the videos in ActivityNet to form a new dataset for video re-localization research, which consists of about 10, 000 videos of diverse visual appearances associated with localized boundary information.

1

# The 2021 Image Similarity Dataset and Challenge

17 Jun 2021

This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).

1

# Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?

5 Oct 2021

Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market.

1

# D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection

13 Nov 2021

In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21.

1

# Bag of Tricks and A Strong baseline for Image Copy Detection

13 Nov 2021

In this paper, a bag of tricks and a strong baseline are proposed for image copy detection.

1

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

8 Dec 2021

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.

1

# Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

16 Feb 2022

Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.

1

# A Self-Supervised Descriptor for Image Copy Detection

We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling operator from the instance matching literature, and adapting contrastive learning to augmentations that combine images.

1