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Image Retrieval

148 papers with code · Computer Vision

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

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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

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

Deep Image Retrieval: Learning global representations for image search

5 Apr 2016tensorflow/models

We propose a novel approach for instance-level image retrieval.

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.

FACE RECOGNITION IMAGE RETRIEVAL

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

Learning Deep Representations of Fine-grained Visual Descriptions

CVPR 2016 hanzhanggit/StackGAN-v2

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information.

IMAGE RETRIEVAL ZERO-SHOT LEARNING

Fine-tuning CNN Image Retrieval with No Human Annotation

3 Nov 2017filipradenovic/cnnimageretrieval-pytorch

We show that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval.

IMAGE RETRIEVAL

CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples

8 Apr 2016filipradenovic/cnnimageretrieval-pytorch

Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in many computer vision tasks.

IMAGE RETRIEVAL

Graph-RISE: Graph-Regularized Image Semantic Embedding

14 Feb 2019tensorflow/neural-structured-learning

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.

IMAGE CLASSIFICATION IMAGE RETRIEVAL