1 citations · 1 across the 4 of their papers we have counts for
6 papers
Koo-Fu CLIP: Closed-Form Adaptation of Vision-Language Models via Fukunaga-Koontz Linear Discriminant Analysis
Matej Suchanek, Klara Janouskova, Ondrej Vasatko +1
Visual-language models such as CLIP provide powerful general-purpose representations, but their raw embeddings are not optimized for supervised classification, often exhibiting lim…
Image Recognition with Vision and Language Embeddings of VLMs
Illia Volkov, Nikita Kisel, Klara Janouskova +1
Vision-language models (VLMs) have enabled strong zero-shot classification through image-text alignment. Yet, their purely visual inference capabilities remain under-explored. In t…
SAM2RL: Towards Reinforcement Learning Memory Control in Segment Anything Model 2
Alen Adamyan, Tomáš Čížek, Matej Straka +2
Segment Anything Model 2 (SAM 2) has demonstrated strong performance in object segmentation tasks and has become the state-of-the-art for visual object tracking. The model stores i…
Flaws of ImageNet, Computer Vision's Favourite Dataset
Nikita Kisel, Illia Volkov, Katerina Hanzelkova +2
Since its release, ImageNet-1k dataset has become a gold standard for evaluating model performance. It has served as the foundation for numerous other datasets and training tasks i…
Bringing the Context Back into Object Recognition, Robustly
Klara Janouskova, Cristian Gavrus, Jiri Matas
In object recognition, both the subject of interest (referred to as foreground, FG, for simplicity) and its surrounding context (background, BG) may play an important role. However…
Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures
Klara Janouskova, Mattia Rigotti, Ioana Giurgiu +1
Labeling images for visual segmentation is a time-consuming task which can be costly, particularly in application domains where labels have to be provided by specialized expert ann…