activity
20212025
most citedSynthetic Data from Diffusion Models Improves ImageNet Classification

79 citations · 196 across the 14 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV2024

Objective drives the consistency of representational similarity across datasets

Laure Ciernik, Lorenz Linhardt, Marco Morik +3

The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irre…

cs.CV20241 cited

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…

cs.CV20243 cited

Aligning Machine and Human Visual Representations across Abstraction Levels

Lukas Muttenthaler, Klaus Greff, Frieda Born +6

Deep neural networks have achieved success across a wide range of applications, including as models of human behavior and neural representations in vision tasks. However, neural ne…

cs.CV20238 cited

Replacing softmax with ReLU in Vision Transformers

Mitchell Wortsman, Jaehoon Lee, Justin Gilmer +1

Previous research observed accuracy degradation when replacing the attention softmax with a point-wise activation such as ReLU. In the context of vision transformers, we find that…

cs.CV202371 cited

OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Anas Awadalla, Irena Gao, Josh Gardner +13

We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replicati…

cs.CV2023

Guiding Image Captioning Models Toward More Specific Captions

Simon Kornblith, Lala Li, Zirui Wang +1

Image captioning is conventionally formulated as the task of generating captions for images that match the distribution of reference image-caption pairs. However, reference caption…