12 citations · 16 across the 13 of their papers we have counts for
8 papers · 1 filter
Probing the Representational Power of Sparse Autoencoders in Vision Models
Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff +4
Sparse Autoencoders (SAEs) have emerged as a popular tool for interpreting the hidden states of large language models (LLMs). By learning to reconstruct activations from a sparse b…
Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders
Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff +4
The ImageNet hierarchy provides a structured taxonomy of object categories, offering a valuable lens through which to analyze the representations learned by deep vision models. In…
FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le +5
Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensurin…
Debias your Large Multi-Modal Model at Test-Time via Non-Contrastive Visual Attribute Steering
Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck +4
Large Multi-Modal Models (LMMs) have demonstrated impressive capabilities as general-purpose chatbots able to engage in conversations about visual inputs. However, their responses…
Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations
Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck +3
Large Vision Language Models (LVLMs) such as LLaVA have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input ima…
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…
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…