Large-scale benchmarks provide a solid foundation for the development of action analytics.
Most of the previous activity benchmarks focus on analyzing actions in RGB videos. There is …

In sign language recognition (SLR) with multimodal data, a sign word can be represented by
multiply features, for which there exist an intrinsic property and a mutually complementary …

A novel multimodal fusion approach is proposed for Chinese sign language (CSL)
recognition. This framework, the LSTM2+ CHMM model, uses dual long short-term memory …

Egocentric early action prediction aims to recognize actions from the first-person view by
only observing a partial video segment, which is challenging due to the limited context …

Recognizing activities from egocentric multimodal data collected by wearable cameras and
sensors, is gaining interest, as multimodal methods always benefit from the complementarity …

It is crucial to sample a small portion of relevant frames for efficient video classification. The
existing methods mainly develop hand-designed sampling strategies or learn sequential …

Multi-view human action recognition remains a challenging problem due to large view
changes. In this article, we propose a transfer learning-based framework called transferable …

Multi-view (representation) learning derives an entity's representation from its multiple
observable views to facilitate various downstream tasks. The most challenging topic is how …

With the advent of egocentric cameras, there are new challenges where traditional computer
vision is not sufficient to handle this kind of video. Moreover, egocentric cameras often offer …

Recent advancements in recurrent neural network (RNN) research have demonstrated the
superiority of utilizing multiscale structures in learning temporal representations of time …