20 papers with code • 1 benchmarks • 2 datasets
LibrariesUse these libraries to find Copy Detection models and implementations
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.
Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market.
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.
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.