Copy Detection
24 papers with code • 1 benchmarks • 2 datasets
Libraries
Use these libraries to find Copy Detection models and implementationsMost implemented papers
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).
Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection
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.
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.
Feature-compatible Progressive Learning for Video Copy Detection
Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content.
AnyPattern: Towards In-context Image Copy Detection
To accommodate the "seen $\rightarrow$ unseen" generalization scenario, we construct the first large-scale pattern dataset named AnyPattern, which has the largest number of tamper patterns ($90$ for training and $10$ for testing) among all the existing ones.
LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers
This paper considers a learnable approach for comparing and aligning videos.
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.
The 2021 Image Similarity Dataset and Challenge
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?
Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market.
D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection
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.