12 citations · 22 across the 10 of their papers we have counts for
5 papers · 1 filter
Visual prompt engineering for video models
Robert Geirhos, Yuxuan Li, Thaddäus Wiedemer +7
In the age of foundation models, a model is only as good as its prompt. For this reason, prompt engineering has become an essential technique for improving language model performan…
Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning
Neha Kalibhat, Priyatham Kattakinda, Sumit Nawathe +5
Vision transformers have established a precedent of patchifying images into uniformly-sized chunks before processing. We hypothesize that this design choice may limit models in lea…
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations
Neha Kalibhat, Warren Morningstar, Alex Bijamov +3
Self-Supervised Learning (SSL) enables training performant models using limited labeled data. One of the pillars underlying vision SSL is the use of data augmentations/perturbation…
Adapting Self-Supervised Representations to Multi-Domain Setups
Neha Kalibhat, Sam Sharpe, Jeremy Goodsitt +2
Current state-of-the-art self-supervised approaches, are effective when trained on individual domains but show limited generalization on unseen domains. We observe that these model…
Identifying Interpretable Subspaces in Image Representations
Neha Kalibhat, Shweta Bhardwaj, Bayan Bruss +3
We propose Automatic Feature Explanation using Contrasting Concepts (FALCON), an interpretability framework to explain features of image representations. For a target feature, FALC…