29 citations · 61 across the 14 of their papers we have counts for
5 papers · 1 filter
Does Data Scaling Lead to Visual Compositional Generalization?
Arnas Uselis, Andrea Dittadi, Seong Joon Oh
Compositional understanding is crucial for human intelligence, yet it remains unclear whether contemporary vision models exhibit it. The dominant machine learning paradigm is built…
On the rankability of visual embeddings
Ankit Sonthalia, Arnas Uselis, Seong Joon Oh
We study whether visual embedding models capture continuous, ordinal attributes along linear directions, which we term _rank axes_. We define a model as _rankable_ for an attribute…
Diffusion Classifiers Understand Compositionality, but Conditions Apply
Yujin Jeong, Arnas Uselis, Seong Joon Oh +1
Understanding visual scenes is fundamental to human intelligence. While discriminative models have significantly advanced computer vision, they often struggle with compositional un…
Intermediate Layer Classifiers for OOD generalization
Arnas Uselis, Seong Joon Oh
Deep classifiers are known to be sensitive to data distribution shifts, primarily due to their reliance on spurious correlations in training data. It has been suggested that these…
CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally
Darina Koishigarina, Arnas Uselis, Seong Joon Oh
CLIP (Contrastive Language-Image Pretraining) has become a popular choice for various downstream tasks. However, recent studies have questioned its ability to represent composition…