12 papers
It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models
Anne Harrington, A. Sophia Koepke, Shyamgopal Karthik +2
Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prompt. Previous work has attempted…
Personalizing Text-to-Image Generation to Individual Taste
Anne-Sofie Maerten, Juliane Verwiebe, Shyamgopal Karthik +3
Modern text-to-image (T2I) models generate high-fidelity visuals but remain indifferent to individual user preferences. While existing reward models optimize for "average" human ap…
Post-hoc Probabilistic Vision-Language Models
Anton Baumann, Rui Li, Marcus Klasson +5
Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map image…
Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models
Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot +2
Sparse Autoencoders (SAEs) have recently gained attention as a means to improve the interpretability and steerability of Large Language Models (LLMs), both of which are essential f…
Solving Spatial Supersensing Without Spatial Supersensing
Vishaal Udandarao, Shyamgopal Karthik, Surabhi S. Nath +3
Cambrian-S aims to take the first steps towards improving video world models with spatial supersensing by introducing (i) two benchmarks, VSI-Super-Recall (VSR) and VSI-Super-Count…
Simplifying Knowledge Transfer in Pretrained Models
Siddharth Jain, Shyamgopal Karthik, Vineet Gandhi
Pretrained models are ubiquitous in the current deep learning landscape, offering strong results on a broad range of tasks. Recent works have shown that models differing in various…