Energy-efficient and interpretable multisensor human activity recognition via deep fused lasso net
Utilizing data acquired by multiple wearable sensors can usually guarantee more accurate
recognition for deep learning based human activity recognition. However, an increased …
recognition for deep learning based human activity recognition. However, an increased …
Fedmekt: Distillation-based embedding knowledge transfer for multimodal federated learning
Federated learning (FL) enables a decentralized machine learning paradigm for multiple
clients to collaboratively train a generalized global model without sharing their private data …
clients to collaboratively train a generalized global model without sharing their private data …
Wear: An outdoor sports dataset for wearable and egocentric activity recognition
Research has shown the complementarity of camera-and inertial-based data for modeling
human activities, yet datasets with both egocentric video and inertial-based sensor data …
human activities, yet datasets with both egocentric video and inertial-based sensor data …
Eqa-mx: Embodied question answering using multimodal expression
Humans predominantly use verbal utterances and nonverbal gestures (eg, eye gaze and
pointing gestures) in their natural interactions. For instance, pointing gestures and verbal …
pointing gestures) in their natural interactions. For instance, pointing gestures and verbal …
M3sense: Affect-agnostic multitask representation learning using multimodal wearable sensors
Modern smartwatches or wrist wearables having multiple physiological sensing modalities
have emerged as a subtle way to detect different mental health conditions, such as anxiety …
have emerged as a subtle way to detect different mental health conditions, such as anxiety …
Patron: perspective-aware multitask model for referring expression grounding using embodied multimodal cues
Humans naturally use referring expressions with verbal utterances and nonverbal gestures
to refer to objects and events. As these referring expressions can be interpreted differently …
to refer to objects and events. As these referring expressions can be interpreted differently …
CAESAR: An embodied simulator for generating multimodal referring expression datasets
MM Islam, R Mirzaiee, A Gladstone… - Advances in Neural …, 2022 - proceedings.neurips.cc
Humans naturally use verbal utterances and nonverbal gestures to refer to various objects
(known as $\textit {referring expressions} $) in different interactional scenarios. As collecting …
(known as $\textit {referring expressions} $) in different interactional scenarios. As collecting …
Maven: A memory augmented recurrent approach for multimodal fusion
Multisensory systems provide complementary information that aids many machine learning
approaches in perceiving the environment comprehensively. These systems consist of …
approaches in perceiving the environment comprehensively. These systems consist of …
VADER: Vector-Quantized Generative Adversarial Network for Motion Prediction
Human motion prediction is an essential component for enabling close-proximity human-
robot collaboration. The task of accurately predicting human motion is non-trivial and is …
robot collaboration. The task of accurately predicting human motion is non-trivial and is …
IMPRINT: Interactional dynamics-aware motion prediction in teams using multimodal context
Robots are moving from working in isolation to working with humans as a part of human-
robot teams. In such situations, they are expected to work with multiple humans and need to …
robot teams. In such situations, they are expected to work with multiple humans and need to …