Spatially dense self-supervised learning is a rapidly growing problem domain with
promising applications for unsupervised segmentation and pretraining for dense …

Abstract We introduce AutoVER, an Autoregressive model for Visual Entity Recognition. Our
model extends an autoregressive Multimodal Large Language Model by employing retrieval …

Self-supervised learning (SSL) has revolutionized visual representation learning, but has
not achieved the robustness of human vision. A reason for this could be that SSL does not …

Facial Action Unit (AU) detection has gained significant research attention as AUs contain
complex expression information. In this paper, we unpack two fundamental factors in AU …

Video Foundation Models (ViFMs) aim to develop general-purpose representations for
various video understanding tasks by leveraging large-scale datasets and powerful models …

The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes
the refereed proceedings of the 18th European Conference on Computer Vision, ECCV …

This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable
facial representation that boosts various face security tasks with respect to generalization …

Current video-based Masked Autoencoders (MAEs) primarily focus on learning effective
spatiotemporal representations from a visual perspective, which may lead the model to …

Remote sensing offers vast yet sparsely labeled multimodal data but lacks foundation
models that can be leveraged across societally impactful applications. In this thesis, I …