🚀 new state-of-the-art on ILIAS dataset!

Curious how well the latest models can recognize particular objects?
We evaluated the base and large variants of DINOv3 and Perception Encoder (PE) on instance-level image retrieval.

See the results 👉 vrg.fel.cvut.cz/ilias/

ILIAS is a large-scale test dataset for evaluation on Instance-Level Image retrieval At Scale. It is designed to support future research in image-to-image and text-to-image retrieval for particular objects and serves as a benchmark for evaluating foundation models and retrieval techniques.

For PhD and MSc students interested in a research visit to Prague/VRG in 2025: we're open to hosting short-term collaborations or internships on a range of computer vision topics. If this sounds exciting, reach out by e-mail! We'd love to discuss potential projects. Some examples 🧵
#Internship #CV

Deep global descriptors give a convenient way for retrieval, but local descriptors are a game changer in finding needles in a haystack (particular objects in clutter). Due to their high cost, with AMES we optimize the performance/memory trade-off during re-ranking. #ECCV2024

Arxiv paper: arxiv.org/abs/2408.03282
Code: github.com/pavelsuma/ames