Towards A Fairer Landmark Recognition Dataset
We introduce a new landmark recognition dataset, which is created with a focus on fair worldwide representation. While previous work proposes to collect as many images as possible from web repositories, we instead argue that such approaches can lead to biased data. To create a more comprehensive and equitable dataset, we start by defining the fair relevance of a landmark to the world population. These relevances are estimated by combining anonymized Google Maps user contribution statistics with the contributors' demographic information. We present a stratification approach and analysis which leads to a much fairer coverage of the world, compared to existing datasets. The resulting datasets are used to evaluate computer vision models as part of the the Google Landmark Recognition and RetrievalChallenges 2021.
Code (0)
등록된 구현이 없습니다.
Tasks
Landmark RecognitionSimilar Papers 제목 키워드 기반
Improving Fairness in Large-Scale Object Recognition by CrowdSourced Demographic Information
There has been increasing awareness of ethical issues in machine learning, and fairness has become an important research topic. Most fairness efforts in computer vision have been focused on human sensing applications and…
Cultural Vocal Bursts Intensity PredictionFairnessLandmark RecognitionObject RecognitionLarge-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
The Google-Landmarks-v2 dataset is the biggest worldwide landmarks dataset characterized by a large magnitude of noisiness and diversity. We present a novel landmark retrieval/recognition system, robust to a noisy and di…
DiversityLandmark RecognitionMetric LearningRe-Ranking+1Toward Fairer Face Recognition Datasets
Face recognition and verification are two computer vision tasks whose performance has progressed with the introduction of deep representations. However, ethical, legal, and technical challenges due to the sensitive chara…
Face RecognitionFairnessLAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a va…
DiversityFace RecognitionSelf-Supervised Learning2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
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 RecognitionRerankingRetrieval