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 with no code
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.
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
Experimental results show that method (i) improves the audio-text retrieval performance by selecting the nearest image that aligns with the audio information and transferring the learned knowledge.
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer
Rather than relying on the availability of private data of teachers as in existing methods, we propose to exploit an assumption that holds universally in self-localization tasks: "The teacher model is a self-localization system" and to reuse the self-localization system of a teacher as a sole accessible communication channel.
You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval
Two primary input modalities prevail in image retrieval: sketch and text.
Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers
This paper, for the first time, explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR).
How to Handle Sketch-Abstraction in Sketch-Based Image Retrieval?
@q loss to inject that understanding into the system.
