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Image retrieval systems aim to find similar images to a query image among an image dataset.

( Image credit: DELF )

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Datasets

Greatest papers with code

Detect-to-Retrieve: Efficient Regional Aggregation for Image Search

CVPR 2019 tensorflow/models

Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods.

IMAGE RETRIEVAL

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

CVPR 2018 tensorflow/models

In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the ground truth.

IMAGE RETRIEVAL

Unifying Deep Local and Global Features for Image Search

ECCV 2020 tensorflow/models

Image retrieval is the problem of searching an image database for items that are similar to a query image.

DIMENSIONALITY REDUCTION IMAGE RETRIEVAL

Large-Scale Image Retrieval with Attentive Deep Local Features

ICCV 2017 tensorflow/models

We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature).

IMAGE RETRIEVAL

VGGFace2: A dataset for recognising faces across pose and age

23 Oct 2017deepinsight/insightface

The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimize the label noise.

Ranked #3 on Face Verification on IJB-C (TAR @ FAR=0.01 metric)

FACE RECOGNITION FACE VERIFICATION IMAGE RETRIEVAL

Kornia: an Open Source Differentiable Computer Vision Library for PyTorch

5 Oct 2019kornia/kornia

This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.

EDGE DETECTION IMAGE AUGMENTATION IMAGE CROPPING IMAGE DENOISING IMAGE MANIPULATION IMAGE MORPHING IMAGE RETRIEVAL IMAGE SMOOTHING IMAGE STITCHING

Improving zero-shot learning by mitigating the hubness problem

20 Dec 2014facebookresearch/MUSE

The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels.

IMAGE RETRIEVAL ZERO-SHOT LEARNING

Layer Normalization

21 Jul 2016lab-ml/nn

One way to reduce the training time is to normalize the activities of the neurons.

IMAGE RETRIEVAL