Computer Vision
 
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Image Retrieval

666 papers with code • 54 benchmarks • 75 datasets

Image Retrieval is a fundamental and long-standing computer vision task that involves finding images similar to a provided query from a large database. It's often considered as a form of fine-grained, instance-level classification. Not just integral to image recognition alongside classification and detection, it also holds substantial business value by helping users discover images aligning with their interests or requirements, guided by visual similarity or other parameters.

( Image credit: DELF )

Libraries

Use these libraries to find Image Retrieval models and implementations
huggingface/transformers
4 papers
124,984
OML-Team/open-metric-learning
4 papers
762
kornia/kornia
2 papers
9,377
salesforce/lavis
2 papers
8,724
See all 10 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Emerging Properties in Self-Supervised Vision Transformers

facebookresearch/dino • • ICCV 2021

In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets).

26
Paper
Code

VGGFace2: A dataset for recognising faces across pose and age

deepinsight/insightface • • 23 Oct 2017

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.

24
Paper
Code

NetVLAD: CNN architecture for weakly supervised place recognition

Relja/netvlad • CVPR 2016

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph.

15
Paper
Code

Fine-tuning CNN Image Retrieval with No Human Annotation

filipradenovic/cnnimageretrieval-pytorch • • 3 Nov 2017

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.

13
Paper
Code

Large-Scale Image Retrieval with Attentive Deep Local Features

tensorflow/models • • ICCV 2017

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

12
Paper
Code

Circle Loss: A Unified Perspective of Pair Similarity Optimization

layumi/Person_reID_baseline_pytorch • • CVPR 2020

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.

12
Paper
Code

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

salesforce/lavis • • 30 Jan 2023

The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models.

12
Paper
Code

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

facebookresearch/vilbert-multi-task • • NeurIPS 2019

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language.

11
Paper
Code

DINOv2: Learning Robust Visual Features without Supervision

facebookresearch/dinov2 • • 14 Apr 2023

The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.

11
Paper
Code

VSE++: Improving Visual-Semantic Embeddings with Hard Negatives

fartashf/vsepp • • 18 Jul 2017

We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.

10
Paper
Code
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