Search Results for author: Matthias De Lange

Found 12 papers, 9 papers with code

Towards Open-World Gesture Recognition

no code implementations20 Jan 2024 Junxiao Shen, Matthias De Lange, Xuhai "Orson" Xu, Enmin Zhou, Ran Tan, Naveen Suda, Maciej Lazarewicz, Per Ola Kristensson, Amy Karlson, Evan Strasnick

We propose leveraging continual learning to make machine learning models adaptive to new tasks without degrading performance on previously learned tasks.

Continual Learning Gesture Recognition

Multiscale Video Pretraining for Long-Term Activity Forecasting

no code implementations24 Jul 2023 Reuben Tan, Matthias De Lange, Michael Iuzzolino, Bryan A. Plummer, Kate Saenko, Karl Ridgeway, Lorenzo Torresani

To alleviate this issue, we propose Multiscale Video Pretraining (MVP), a novel self-supervised pretraining approach that learns robust representations for forecasting by learning to predict contextualized representations of future video clips over multiple timescales.

Action Anticipation Long Term Action Anticipation

EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video

1 code implementation11 Jul 2023 Matthias De Lange, Hamid Eghbalzadeh, Reuben Tan, Michael Iuzzolino, Franziska Meier, Karl Ridgeway

We introduce an evaluation framework that directly exploits the user's data stream with new metrics to measure the adaptation gain over the population model, online generalization, and hindsight performance.

Action Recognition Continual Learning

CLAD: A realistic Continual Learning benchmark for Autonomous Driving

1 code implementation7 Oct 2022 Eli Verwimp, Kuo Yang, Sarah Parisot, Hong Lanqing, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection.

Autonomous Driving Continual Learning +3

Continual evaluation for lifelong learning: Identifying the stability gap

1 code implementation26 May 2022 Matthias De Lange, Gido van de Ven, Tinne Tuytelaars

Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks, except that this forgetting is temporary and followed by a phase of performance recovery.

Continual Learning Incremental Learning +1

Rehearsal revealed: The limits and merits of revisiting samples in continual learning

1 code implementation ICCV 2021 Eli Verwimp, Matthias De Lange, Tinne Tuytelaars

Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research.

Continual Learning

Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

1 code implementation CVPR 2020 Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.

Continual Learning Domain Adaptation +2

A continual learning survey: Defying forgetting in classification tasks

1 code implementation18 Sep 2019 Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.

Classification Continual Learning +2

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