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Landmark Recognition - Papers With Code Supporting large-scale image recognition with out-of-domain samples psinger kaggle-landmark-recognition-2020-1st-place • • 4 Oct 2020 This article presents an efficient end-to-end method to perform instance-level recognition employed to the task of labeling and ranking landmark images
Google Landmarks Dataset v2 - Papers With Code While image retrieval and instance recognition techniques are progressing rapidly, there is a need for challenging datasets to accurately measure their performance -- while posing novel challenges that are relevant for practical applications
Google Landmarks Dataset v2 - Papers with Code This is the second version of the Google Landmarks dataset (GLDv2), which contains images annotated with labels representing human-made and natural landmarks The dataset can be used for landmark recognition and retrieval experiments
Landmark Recognition - Papers With Code We showcase its application to the landmark recognition domain, presenting a detailed analysis and the final fairer landmark rankings
Facial Landmark Detection - Papers With Code The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various applications, such as face recognition, facial expression analysis, and head pose estimation
Clusformer: A Transformer Based Clustering Approach to Unsupervised . . . The proposed method is evaluated on two popular large-scale visual databases, i e Google Landmark and MS-Celeb-1M face database, and outperforms prior unsupervised clustering methods Code will be available at https: github com VinAIResearch Clusformer
A location-aware embedding technique for accurate landmark recognition The current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD All these techniques do not exploit spatial information, i e consider all the features and the corresponding descriptors without embedding their location in the image
PRAM: Place Recognition Anywhere Model for Efficient Visual . . . In this paper, we propose the place recognition anywhere model (PRAM), a new framework, to perform visual localization efficiently and accurately by recognizing 3D landmarks Specifically, PRAM first generates landmarks directly in 3D space in a self-supervised manner