Learning to recognize novel concepts from just a few image samples is very challenging as
the learned model is easily overfitted on the few data and results in poor generalizability …

This article introduces a versatile multi-task learning framework (UMT-Net) and an adaptive
task weighting (ATW) training method, specifically designed for resource-constrained …

Three-dimensional convolutional neural networks (3D-CNNs) and full connection long short-
term memory networks (FC-LSTMs) have been demonstrated as a kind of powerful non …

Multimodal sensors, including vision sensors and wearable sensors, offer valuable
complementary information for accurate recognition tasks. Nonetheless, the heterogeneity …

In response to the problem where many existing research models only consider acquiring
the temporal information between sequences of continuous skeletons and in response to the …

Human-centric content-based video retrieval has emerged as a prominent area of research
due to its diverse applications. However, this task presents several inherent challenges …

The Contrastive Language-Image Pre-training (CLIP) model achieves strong generalization
by using a large number of text-image pairs for contrastive learning. However, when it is …

Behavior detection receives considerable attention in real-life human–computer interaction,
where the complexity of background information and the variable durations of movements …

As a hot topic in the field of computer vision, video action recognition has great application
potential, such as intelligent monitoring, data recommendation and virtual reality. However …

Due to the distinctive characteristics of sensors, each modality exhibits unique physical
properties. For this reason, in the context of multi-modal action recognition, it is important to …