17 citations · 17 across the 11 of their papers we have counts for
8 papers · 1 filter
When Do Diffusion Models learn to Generate Multiple Objects?
Yujin Jeong, Arnas Uselis, Iro Laina +2
Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical evidence of these failures, th…
How can embedding models bind concepts?
Arnas Uselis, Darina Koishigarina, Seong Joon Oh
Humans easily determine which color belongs to which shape in multi-object scenes, an ability known as concept binding. Vision-language embedding models such as CLIP struggle with…
Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models
Fabian Morelli, Arnas Uselis, Ankit Sonthalia +1
Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these models to improve downstream…
Half-Truths Break Similarity-Based Retrieval
Bora Kargi, Arnas Uselis, Seong Joon Oh
When a text description is extended with an additional detail, image-text similarity should drop if that detail is wrong. We show that CLIP-style dual encoders often violate this i…
Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models
Arnas Uselis, Andrea Dittadi, Seong Joon Oh
Compositional generalization, the ability to recognize familiar parts in novel contexts, is a defining property of intelligent systems. Although modern models are trained on massiv…
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…