5 citations · 11 across the 7 of their papers we have counts for
8 papers
Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability
Shobhita Sundaram, John Quan, Ariel Kwiatkowski +3
RL methods for scaling large reasoning models stall on datasets with low initial success rates, and thus little training signal. We investigate a fundamental question: Can a pretra…
Personalized Representation from Personalized Generation
Shobhita Sundaram, Julia Chae, Yonglong Tian +2
Modern vision models excel at general purpose downstream tasks. It is unclear, however, how they may be used for personalized vision tasks, which are both fine-grained and data-sca…
What Makes for a Good Stereoscopic Image?
Netanel Y. Tamir, Shir Amir, Ranel Itzhaky +8
With rapid advancements in virtual reality (VR) headsets, effectively measuring stereoscopic quality of experience (SQoE) has become essential for delivering immersive and comforta…
When Does Perceptual Alignment Benefit Vision Representations?
Shobhita Sundaram, Stephanie Fu, Lukas Muttenthaler +5
Humans judge perceptual similarity according to diverse visual attributes, including scene layout, subject location, and camera pose. Existing vision models understand a wide range…
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Stephanie Fu, Netanel Tamir, Shobhita Sundaram +4
Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture…
Do Neural Networks for Segmentation Understand Insideness?
Kimberly Villalobos, Vilim Štih, Amineh Ahmadinejad +6
The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentati…