116 citations · 212 across the 9 of their papers we have counts for
10 papers
Multiscale Video Pretraining for Long-Term Activity Forecasting
Reuben Tan, Matthias De Lange, Michael Iuzzolino +4
Long-term activity forecasting is an especially challenging research problem because it requires understanding the temporal relationships between observed actions, as well as the v…
EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video
Matthias De Lange, Hamid Eghbalzadeh, Reuben Tan +3
In egocentric action recognition a single population model is typically trained and subsequently embodied on a head-mounted device, such as an augmented reality headset. While this…
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors
Satoshi Tsutsui, Ruta Desai, Karl Ridgeway
Understanding users' activities from head-mounted cameras is a fundamental task for Augmented and Virtual Reality (AR/VR) applications. A typical approach is to train a classifier…
Unifying Few- and Zero-Shot Egocentric Action Recognition
Tyler R. Scott, Michael Shvartsman, Karl Ridgeway
Although there has been significant research in egocentric action recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed set of action classes. Fixed-set cla…
Stochastic Prototype Embeddings
Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
Supervised deep-embedding methods project inputs of a domain to a representational space in which same-class instances lie near one another and different-class instances lie far ap…
Open-Ended Content-Style Recombination Via Leakage Filtering
Karl Ridgeway, Michael C. Mozer
We consider visual domains in which a class label specifies the content of an image, and class-irrelevant properties that differentiate instances constitute the style. We present a…
Adapted Deep Embeddings: A Synthesis of Methods for -Shot Inductive Transfer Learning
Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
The focus in machine learning has branched beyond training classifiers on a single task to investigating how previously acquired knowledge in a source domain can be leveraged to fa…
Learning Deep Disentangled Embeddings with the F-Statistic Loss
Karl Ridgeway, Michael C. Mozer
Deep-embedding methods aim to discover representations of a domain that make explicit the domain's class structure and thereby support few-shot learning. Disentangling methods aim…
A Survey of Inductive Biases for Factorial Representation-Learning
Karl Ridgeway
With the resurgence of interest in neural networks, representation learning has re-emerged as a central focus in artificial intelligence. Representation learning refers to the disc…
Learning to Generate Images with Perceptual Similarity Metrics
Jake Snell, Karl Ridgeway, Renjie Liao +3
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compa…