12 citations · 16 across the 5 of their papers we have counts for
5 papers
Why do LLaVA Vision-Language Models Reply to Images in English?
Musashi Hinck, Carolin Holtermann, Matthew Lyle Olson +6
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significant…
L-MAGIC: Language Model Assisted Generation of Images with Coherence
Zhipeng Cai, Matthias Mueller, Reiner Birkl +6
In the current era of generative AI breakthroughs, generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterativ…
LDM3D-VR: Latent Diffusion Model for 3D VR
Gabriela Ben Melech Stan, Diana Wofk, Estelle Aflalo +4
Latent diffusion models have proven to be state-of-the-art in the creation and manipulation of visual outputs. However, as far as we know, the generation of depth maps jointly with…
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
Xiao Xu, Bei Li, Chenfei Wu +6
Two-Tower Vision-Language (VL) models have shown promising improvements on various downstream VL tasks. Although the most advanced work improves performance by building bridges bet…
LDM3D: Latent Diffusion Model for 3D
Gabriela Ben Melech Stan, Diana Wofk, Scottie Fox +8
This research paper proposes a Latent Diffusion Model for 3D (LDM3D) that generates both image and depth map data from a given text prompt, allowing users to generate RGBD images f…