Search Results for author:
Found 5 papers, 2 papers with code
LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models
In this work, we present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models.
Getting it Right: Improving Spatial Consistency in Text-to-Image Models
One of the key shortcomings in current text-to-image (T2I) models is their inability to consistently generate images which faithfully follow the spatial relationships specified in the text prompt.
LDM3D-VR: Latent Diffusion Model for 3D VR
Our models are fine-tuned from existing pretrained models on datasets containing panoramic/high-resolution RGB images, depth maps and captions.
LDM3D: Latent Diffusion Model for 3D
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 from text prompts.
MuMUR : Multilingual Multimodal Universal Retrieval
In this paper, we propose a framework MuMUR, that utilizes knowledge transfer from a multilingual model to boost the performance of multi-modal (image and video) retrieval.
