3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CV2025
MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs
Erik Daxberger, Nina Wenzel, David Griffiths +8
Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-q…
cs.CV2024
MM-Ego: Towards Building Egocentric Multimodal LLMs for Video QA
Hanrong Ye, Haotian Zhang, Erik Daxberger +9
This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as th…
cs.CV2023★ 3 cited
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
Erik Daxberger, Floris Weers, Bowen Zhang +7
Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of t…