15 citations · 15 across the 6 of their papers we have counts for
6 papers
Infusing fine-grained visual knowledge to Vision-Language Models
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, André Araujo +1
Large-scale contrastive pre-training produces powerful Vision-and-Language Models (VLMs) capable of generating representations (embeddings) effective for a wide variety of visual a…
ILIAS: Instance-Level Image retrieval At Scale
Giorgos Kordopatis-Zilos, Vladan Stojnić, Anna Manko +7
This work introduces ILIAS, a new test dataset for Instance-Level Image retrieval At Scale. It is designed to evaluate the ability of current and future foundation models and retri…
Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification
Nikolaos-Antonios Ypsilantis, Ondřej Chum
This work proposes a novel method for object co-segmentation, i.e. pixel-level localization of a common object in a set of images, that uses no pixel-level supervision for training…
UDON: Universal Dynamic Online distillatioN for generic image representations
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, André Araujo +1
Universal image representations are critical in enabling real-world fine-grained and instance-level recognition applications, where objects and entities from any domain must be ide…
Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations
Nikolaos-Antonios Ypsilantis, Kaifeng Chen, Bingyi Cao +7
Fine-grained and instance-level recognition methods are commonly trained and evaluated on specific domains, in a model per domain scenario. Such an approach, however, is impractica…
The Met Dataset: Instance-level Recognition for Artworks
Nikolaos-Antonios Ypsilantis, Noa Garcia, Guangxing Han +3
This work introduces a dataset for large-scale instance-level recognition in the domain of artworks. The proposed benchmark exhibits a number of different challenges such as large…