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Found 5 papers, 4 papers with code
ILIAS: Instance-Level Image retrieval At Scale
An extensive benchmarking is performed with the following observations: i) models fine-tuned on specific domains, such as landmarks or products, excel in that domain but fail on ILIAS ii) learning a linear adaptation layer using multi-domain class supervision results in performance improvements, especially for vision-language models iii) local descriptors in retrieval re-ranking are still a key ingredient, especially in the presence of severe background clutter iv) the text-to-image performance of the vision-language foundation models is surprisingly close to the corresponding image-to-image case.
Composed Image Retrieval for Training-Free Domain Conversion
This work addresses composed image retrieval in the context of domain conversion, where the content of a query image is retrieved in the domain specified by the query text.
Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization
The method that achieves the best performance on the augmented validation is selected from the proposed family.
Ranked #1 on
Single-Source Domain Generalization
on Digits-five
Composed Image Retrieval for Remote Sensing
Various attributes can be modified by the textual part, such as shape, color, or context.
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images.
Ranked #1 on
Image to sketch recognition
on Im4Sketch
