4 citations · 6 across the 13 of their papers we have counts for
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Mirage Probes: How Vision Models Fake Visual Understanding
Daniel Ben-Levi, Judah Goldfeder, Weiliang Zhao +5
Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided. This mirage behavior inflates benchmark scores with…
Beyond Cropping and Rotation: Automated Evolution of Powerful Task-Specific Augmentations with Generative Models
Judah Goldfeder, Shreyes Kaliyur, Vaibhav Sourirajan +5
Data augmentation has long been a cornerstone for reducing overfitting in vision models, with methods like AutoAugment automating the design of task-specific augmentations. Recent…
ArticFlow: Generative Simulation of Articulated Mechanisms
Jiong Lin, Jinchen Ruan, Hod Lipson
Recent advances in generative models have produced strong results for static 3D shapes, whereas articulated 3D generation remains challenging due to action-dependent deformations a…
Bi-Encoder Contrastive Learning for Fingerprint and Iris Biometrics
Matthew So, Judah Goldfeder, Mark Lis +1
There has been a historic assumption that the biometrics of an individual are statistically uncorrelated. We test this assumption by training Bi-Encoder networks on three verificat…
LyTimeT: Towards Robust and Interpretable State-Variable Discovery
Kuai Yu, Crystal Su, Xiang Liu +3
Extracting the true dynamical variables of a system from high-dimensional video is challenging due to distracting visual factors such as background motion, occlusions, and texture…
High-Degrees-of-Freedom Dynamic Neural Fields for Robot Self-Modeling and Motion Planning
Lennart Schulze, Hod Lipson
A robot self-model is a task-agnostic representation of the robot's physical morphology that can be used for motion planning tasks in the absence of a classical geometric kinematic…