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

11/13/2021
by   Wenhao Wang, et al.
0

Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D^2LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D^2LV, unsupervised pre-training substitutes the commonly-used supervised one. When training, we design a set of basic and six advanced transformations, and a simple but effective baseline learns robust representation. During testing, a global-local and local-global matching strategy is proposed. The strategy performs local-verification between reference and query images. Experiments demonstrate that the proposed method is effective. The proposed approach ranks first out of 1,103 participants on the Facebook AI Image Similarity Challenge: Matching Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track1-Submission.

READ FULL TEXT

page 2

page 3

page 4

research
11/13/2021

Bag of Tricks and A Strong baseline for Image Copy Detection

Image copy detection is of great importance in real-life social media. I...
research
06/17/2021

The 2021 Image Similarity Dataset and Challenge

This paper introduces a new benchmark for large-scale image similarity d...
research
12/08/2021

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 modif...
research
12/04/2021

3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity Challenge

As a basic task of computer vision, image similarity retrieval is facing...
research
02/08/2022

Results and findings of the 2021 Image Similarity Challenge

The 2021 Image Similarity Challenge introduced a dataset to serve as a n...
research
06/23/2023

LightGlue: Local Feature Matching at Light Speed

We introduce LightGlue, a deep neural network that learns to match local...
research
12/06/2021

Producing augmentation-invariant embeddings from real-life imagery

This article presents an efficient way to produce feature-rich, high-dim...

Please sign up or login with your details

Forgot password? Click here to reset