Image Retrieval
672 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 with no code
Do Vision-Language Models Understand Compound Nouns?
We curate Compun, a novel benchmark with 400 unique and commonly used CNs, to evaluate the effectiveness of VLMs in interpreting CNs.
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Image retrieval, i. e., finding desired images given a reference image, inherently encompasses rich, multi-faceted search intents that are difficult to capture solely using image-based measures.
AIR-HLoc: Adaptive Image Retrieval for Efficient Visual Localisation
State-of-the-art (SOTA) hierarchical localisation pipelines (HLoc) rely on image retrieval (IR) techniques to establish 2D-3D correspondences by selecting the $k$ most similar images from a reference image database for a given query image.
Knowledge-Enhanced Dual-stream Zero-shot Composed Image Retrieval
The pseudo-word tokens generated in this stream are explicitly aligned with fine-grained semantics in the text embedding space.
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals.
Piecewise-Linear Manifolds for Deep Metric Learning
For this purpose, we propose to model the high-dimensional data manifold using a piecewise-linear approximation, with each low-dimensional linear piece approximating the data manifold in a small neighborhood of a point.
A Multimodal Approach for Cross-Domain Image Retrieval
With the latest AI technology, millions of high quality images are being generated by the public, which are constantly motivating the research community to push the limits of generative models to create more complex and realistic images.
Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval
In this study, we explore the efficacy of employing high-resolution features learned through state-of-the-art techniques for image retrieval tasks.
Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval
Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities.
Vector search with small radiuses
Although convenient to compute, this metric is distantly related to the end-to-end accuracy of a full system that integrates vector search.
