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 )
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
Semantically-correlated memories in a dense associative model
I introduce a novel associative memory model named Correlated Dense Associative Memory (CDAM), which integrates both auto- and hetero-association in a unified framework for continuous-valued memory patterns.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Deep quantization methods have shown high efficiency on large-scale image retrieval.
On Train-Test Class Overlap and Detection for Image Retrieval
How important is it for training and evaluation sets to not have class overlap in image retrieval?
Long-CLIP: Unlocking the Long-Text Capability of CLIP
Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities.
Enhancing Historical Image Retrieval with Compositional Cues
linty5/ccbir
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In analyzing vast amounts of digitally stored historical image data, existing content-based retrieval methods often overlook significant non-semantic information, limiting their effectiveness for flexible exploration across varied themes.
MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data
Reconstructions of visual perception from brain activity have improved tremendously, but the practical utility of such methods has been limited.
Leveraging Neural Radiance Field in Descriptor Synthesis for Keypoints Scene Coordinate Regression
Classical structural-based visual localization methods offer high accuracy but face trade-offs in terms of storage, speed, and privacy.
Does the Performance of Text-to-Image Retrieval Models Generalize Beyond Captions-as-a-Query?
AU-DIS/ConQA
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European Conference on Information Retrieval 2024
ConQA comprises 30 descriptive and 50 conceptual queries on 43k images with more than 100 manually annotated images per query.
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.
