Image Retrieval
667 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 )
Benchmarks
These leaderboards are used to track progress in Image Retrieval
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Libraries
Use these libraries to find Image Retrieval models and implementationsDatasets
CIFAR-10
MS COCO
CUB-200-2011
Flickr30k
iNaturalist
DeepFashion
NUS-WIDE
YFCC100M
Stanford Online Products
In-Shop
Subtasks
Latest papers
PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models
We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model.
It's All About Your Sketch: Democratising Sketch Control in Diffusion Models
This paper unravels the potential of sketches for diffusion models, addressing the deceptive promise of direct sketch control in generative AI.
EarthLoc: Astronaut Photography Localization by Indexing Earth from Space
Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response.
Leveraging Foundation Models for Content-Based Medical Image Retrieval in Radiology
mic-dkfz/foundation-models-for-cbmir
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Despite these challenges, our research underscores the vast potential of foundation models for CBIR in radiology, proposing a shift towards versatile, general-purpose medical image retrieval systems that do not require specific tuning.
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing
hly1998/brcd
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In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models.
Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy.
Self-supervised Photographic Image Layout Representation Learning
This shortfall makes the learning process for photographic image layouts suboptimal.
Multi-Spectral Remote Sensing Image Retrieval Using Geospatial Foundation Models
ibm/remote-sensing-image-retrieval
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Image retrieval enables an efficient search through vast amounts of satellite imagery and returns similar images to a query.
Structure Similarity Preservation Learning for Asymmetric Image Retrieval
mcc-wh/ssp
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The centroid vectors in the quantizer serve as anchor points in the embedding space of the gallery model to characterize its structure.
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport
hcvlab/protoot
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This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR, which integrates intra-domain feature representation learning and cross-domain alignment into a unified framework.
