565 citations · 1.1k across the 18 of their papers we have counts for
4 papers · 1 filter
TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models
Pooyan Rahmanzadehgervi, Hung Huy Nguyen, Rosanne Liu +2
Multi-head self-attention (MHSA) is a key component of Transformers, a widely popular architecture in both language and vision. Multiple heads intuitively enable different parallel…
What does a platypus look like? Generating customized prompts for zero-shot image classification
Sarah Pratt, Ian Covert, Rosanne Liu +1
Open-vocabulary models are a promising new paradigm for image classification. Unlike traditional classification models, open-vocabulary models classify among any arbitrary set of c…
Natural Adversarial Objects
Felix Lau, Nishant Subramani, Sasha Harrison +3
Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce…
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Rosanne Liu, Joel Lehman, Piero Molino +4
Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional ne…