Matthias Minderer

I am a Senior Research Scientist at Google Brain in Zürich, where I work on computer vision.

Before joining Google, I received a PhD from Harvard University, working with Christopher Harvey. My thesis focused on the representation of visual and action-related information in the mammalian cerebral cortex. Earlier, I studied neuroscience at ETH Zürich and biochemistry at the University of Cambridge.

Email  /  CV  /  Google Scholar

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I'm interested in visual representation learning, specifically how to impart abstract structure and inductive biases to the representations learned by deep neural network to make them more useful, interpretable, and robust.

Selected Publications

For a full list, see Google Scholar.

OWL-ViT v2: Scaling Open-Vocabulary Object Detection
Matthias Minderer, Alexey Gritsenko, Neil Houlsby
preprint, 2023

We scale open-vocabulary object detection through self-training on pseudo-labeled web image-text data.

OWL-ViT: Simple Open-Vocabulary Object Detection with Vision Transformers
Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, Neil Houlsby
ECCV, 2022

We develop a simple transformer-based architecture and training recipe that achieves strong performance in open-vocabulary object detection.

Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
NeurIPS, 2021

We study the uncertainty calibration and its relationship with accuracy of recent state-of-the-art image classification models.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
ICLR, 2021

We show that a pure transformer, applied directly to sequences of image patches, can perform very well on image classification tasks.

Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen
ICML, 2020

We tackle the problem of low-level shortcuts in self-supervised learning by training an adversarial "lens" to remove shortcut features from images.

Unsupervised Learning of Object Structure and Dynamics from Videos
Matthias Minderer, Chen Sun, Ruben Villegas, Kevin Murphy, Honglak Lee
NeurIPS, 2019

By using a spatially structured (keypoint-based) image representation, we improve video prediction quality and the usefulness of the learned video representations.

The Spatial Structure of Neural Encoding in Mouse Posterior Cortex during Navigation
Matthias Minderer, Kristen Brown, Christopher Harvey
Neuron, 2019

Using large-scale neural recordings and deep models of neural encoding, we show that navigation-related information is distributed and varies gradually across large parts of the posterior cortex, even across retinotopic boundaries.

Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex
Laura Driscoll, Noah Pettit, Matthias Minderer, Selmaan Chettih, Christopher Harvey
Cell, 2017

Contrary to the idea that representations of sensory stimuli or the activity patterns that accompany motor actions are stable, neuronal representations in the parietal cortex can change across days, possibly allowing for the tradeoff between stable encoding of information and flexibility for incorporating new information.

Neuroscience: Virtual reality explored
Matthias Minderer, Christopher Harvey, Flavio Donato, Edvard Moser
Nature, 2016

We discuss the advantages of using virtual reality to study sensorimotor representations in the brain.

Chronic imaging of cortical sensory map dynamics using a genetically encoded calcium indicator
Matthias Minderer, Wenrui Liu, Lazar Sumanovski, Sebastian Kügler, Fritjof Helmchen, David Margolis
J Phys, 2012

We present a method for fast fluorescence imaging of map-level cortical activity using a calcium indicator protein. Sensory-evoked neuronal activity can be imaged repeatedly in the same mouse over weeks, enabling new opportunities for the longitudinal study of cortical function and dysfunction.

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