6 papers
Invisible Shortcuts: Why Vision Encoders Know Your Camera
Vladan Stojnić, Ryan Ramos, Giorgos Kordopatis-Zilos +2
Deep vision models exploit shortcuts, relying on cues that correlate with supervision signals. Prior work has focused on visible biases, such as object-background or texture correl…
Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?
Tilemachos Aravanis, Vladan Stojnić, Bill Psomas +2
Open-vocabulary segmentation (OVS) extends the zero-shot recognition capabilities of vision-language models (VLMs) to pixel-level prediction, enabling segmentation of arbitrary cat…
Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders
Faizan Farooq Khan, Vladan Stojnić, Zakaria Laskar +2
This work explores text-to-image retrieval for queries that specify or describe a semantic category. While vision-and-language models (VLMs) like CLIP offer a straightforward open-…
Processing and acquisition traces in visual encoders: What does CLIP know about your camera?
Ryan Ramos, Vladan Stojnić, Giorgos Kordopatis-Zilos +3
Prior work has analyzed the robustness of visual encoders to image transformations and corruptions, particularly in cases where such alterations are not seen during training. When…
LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation
Vladan Stojnić, Yannis Kalantidis, Jiří Matas +1
We propose a training-free method for open-vocabulary semantic segmentation using Vision-and-Language Models (VLMs). Our approach enhances the initial per-patch predictions of VLMs…
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…