3rd Place Solution to "Google Landmark Retrieval 2020"

2020-08-24 · Ke Mei, Lei LI, Jinchang Xu, Yanhua Cheng, Yugeng Lin

Image retrieval is a fundamental problem in computer vision. This paper presents our 3rd place detailed solution to the Google Landmark Retrieval 2020 challenge. We focus on the exploration of data cleaning and models with metric learning. We use a data cleaning strategy based on embedding clustering. Besides, we employ a data augmentation method called Corner-Cutmix, which improves the model's ability to recognize multi-scale and occluded landmark images. We show in detail the ablation experiments and results of our method.

📄 PDF Abstract BibTeX arXiv:2008.10480

Code (1)

Raykoooo/3rd_place_to_Kaggle_Google_Landmark_Retrieval_2020 pytorch

Tasks

ClusteringData Augmentation Image Retrieval Metric Learning Retrieval

2nd Place Solution to Google Landmark Retrieval 2021

2021-10-08 · Zhang Yuqi, Xu Xianzhe, Chen Weihua, Wang Yaohua 외

This paper presents the 2nd place solution to the Google Landmark Retrieval 2021 Competition on Kaggle. The solution is based on a baseline with training tricks from person re-identification, a continent-aware sampling s…

Image Retrieval Person Re-Identification Reranking Retrieval

2nd Place Solution to Google Landmark Retrieval 2020

2022-07-11 · Min Yang, Cheng Cui, Xuetong Xue, Hui Ren 외

This paper presents the 2nd place solution to the Google Landmark Retrieval Competition 2020. We propose a training method of global feature model for landmark retrieval without post-processing, such as local feature and…

Retrieval

1st Place Solution to Google Landmark Retrieval 2020

2020-08-24 · SeungKee Jeon

This paper presents the 1st place solution to the Google Landmark Retrieval 2020 Competition on Kaggle. The solution is based on metric learning to classify numerous landmark classes, and uses transfer learning with two …

Metric Learning Retrieval Transfer Learning

2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019

2019-06-10 · Kaibing Chen, Cheng Cui, Yuning Du, Xianglong Meng 외

We present a retrieval based system for landmark retrieval and recognition challenge.There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augme…

Landmark Recognition Reranking Retrieval

3rd Place Solution to Google Landmark Recognition Competition 2021

2021-10-06 · Cheng Xu, Weimin WANG, Shuai Liu, Yong Wang 외

In this paper, we show our solution to the Google Landmark Recognition 2021 Competition. Firstly, embeddings of images are extracted via various architectures (i.e. CNN-, Transformer- and hybrid-based), which are optimiz…

Landmark Recognition Retrieval