Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Ridgeway, Karl"'
Autor:
Tan, Reuben, De Lange, Matthias, Iuzzolino, Michael, Plummer, Bryan A., Saenko, Kate, Ridgeway, Karl, Torresani, Lorenzo
Long-term activity forecasting is an especially challenging research problem because it requires understanding the temporal relationships between observed actions, as well as the variability and complexity of human activities. Despite relying on stro
Externí odkaz:
http://arxiv.org/abs/2307.12854
Autor:
De Lange, Matthias, Eghbalzadeh, Hamid, Tan, Reuben, Iuzzolino, Michael, Meier, Franziska, Ridgeway, Karl
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 model remains static for new users and environments, we introduce an
Externí odkaz:
http://arxiv.org/abs/2307.05784
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 in a supervised manner using data labeled by humans. This approach has
Externí odkaz:
http://arxiv.org/abs/2110.01680
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 classification is useful for benchmarking methods, but is often unrealist
Externí odkaz:
http://arxiv.org/abs/2006.11393
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 apart. We propose a probabilistic method that treats embeddings as rando
Externí odkaz:
http://arxiv.org/abs/1909.11702
Autor:
Ridgeway, Karl, Mozer, Michael C.
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 domain-independent method that permits the open-ended recombination o
Externí odkaz:
http://arxiv.org/abs/1810.00110
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 facilitate learning in a related target domain, known as inductive trans
Externí odkaz:
http://arxiv.org/abs/1805.08402
Autor:
Ridgeway, Karl, Mozer, Michael C.
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 to make explicit compositional or factorial structure. We combine thes
Externí odkaz:
http://arxiv.org/abs/1802.05312
Autor:
Ridgeway, Karl
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 discovery of useful encodings of data that make domain-relevant informatio
Externí odkaz:
http://arxiv.org/abs/1612.05299
Autor:
Snell, Jake, Ridgeway, Karl, Liao, Renjie, Roads, Brett D., Mozer, Michael C., Zemel, Richard S.
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 compact representation. Supervised training of image-synthesis networks typ
Externí odkaz:
http://arxiv.org/abs/1511.06409