Large-Scale Image Retrieval with Attentive Deep Local Features

Code

• github.com/tensorflow/models
  OfficialIn papertf★ 77,694
• github.com/MindSpore-paper-code-3/code2/tree/main/delf
  mindspore★ 0
• github.com/insikk/delf_enhanced
  tf★ 0
• github.com/MindSpore-paper-code-3/code8/tree/main/delf
  mindspore★ 0
• github.com/jandaldrop/landmark-recognition-challenge
  tf★ 0
• github.com/qianlinjun/delf-pytorch
  pytorch★ 0
• github.com/nashory/DeLF-pytorch
  pytorch★ 0
• github.com/pandigreat/DELF
  pytorch★ 0
• github.com/kingcong/delf
  mindspore★ 0
• github.com/mindspore-ai/models/tree/master/research/cv/delf
  mindspore★ 0

Abstract

We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new feature is based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset. To identify semantically useful local features for image retrieval, we also propose an attention mechanism for keypoint selection, which shares most network layers with the descriptor. This framework can be used for image retrieval as a drop-in replacement for other keypoint detectors and descriptors, enabling more accurate feature matching and geometric verification. Our system produces reliable confidence scores to reject false positives---in particular, it is robust against queries that have no correct match in the database. To evaluate the proposed descriptor, we introduce a new large-scale dataset, referred to as Google-Landmarks dataset, which involves challenges in both database and query such as background clutter, partial occlusion, multiple landmarks, objects in variable scales, etc. We show that DELF outperforms the state-of-the-art global and local descriptors in the large-scale setting by significant margins. Code and dataset can be found at the project webpage: https://github.com/tensorflow/models/tree/master/research/delf .

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