Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Iuzzolino, Michael"'
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
Autor:
Patel, Dhruvesh, Eghbalzadeh, Hamid, Kamra, Nitin, Iuzzolino, Michael Louis, Jain, Unnat, Desai, Ruta
In our pursuit of advancing multi-modal AI assistants capable of guiding users to achieve complex multi-step goals, we propose the task of "Visual Planning for Assistance (VPA)". Given a succinct natural language goal, e.g., "make a shelf", and a vid
Externí odkaz:
http://arxiv.org/abs/2304.09179
Given video demonstrations and paired narrations of an at-home procedural task such as changing a tire, we present an approach to extract the underlying task structure -- relevant actions and their temporal dependencies -- via action-centric task gra
Externí odkaz:
http://arxiv.org/abs/2302.05330
Real world learning scenarios involve a nonstationary distribution of classes with sequential dependencies among the samples, in contrast to the standard machine learning formulation of drawing samples independently from a fixed, typically uniform di
Externí odkaz:
http://arxiv.org/abs/2109.05675
Although deep feedforward neural networks share some characteristics with the primate visual system, a key distinction is their dynamics. Deep nets typically operate in serial stages wherein each layer completes its computation before processing begi
Externí odkaz:
http://arxiv.org/abs/2102.09808
We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting. In this setting, episodes do not have separate training and testing phases, and
Externí odkaz:
http://arxiv.org/abs/2007.04546
We investigate how different active learning (AL) query policies coupled with classification uncertainty visualizations affect analyst trust in automated classification systems. A current standard policy for AL is to query the oracle (e.g., the analy
Externí odkaz:
http://arxiv.org/abs/2004.00762
Publikováno v:
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end. Due to its simplicity late fusion is still the predominant approach in many state-of-the
Externí odkaz:
http://arxiv.org/abs/1911.08670
In human perception and cognition, a fundamental operation that brains perform is interpretation: constructing coherent neural states from noisy, incomplete, and intrinsically ambiguous evidence. The problem of interpretation is well matched to an ea
Externí odkaz:
http://arxiv.org/abs/1906.03504