In this competitive world, the supervision and monitoring of human resources are primary
and necessary tasks to drive context-aware applications. Advancement in sensor and …

Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds
of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) …

Learning to classify video data from classes not included in the training data, ie video-based
zero-shot learning, is challenging. We conjecture that the natural alignment between the …

We present a cross-modal Transformer-based framework, which jointly encodes video data
and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually …

The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown
great potential in extracting audio-visual joint feature representations. However, coupling …

Audio-visual zero-shot learning (ZSL) has attracted board attention, as it could classify video
data from classes that are not observed during training. However, most of the existing …

While video action recognition has been an active area of research for several years, zero-
shot action recognition has only recently started gaining traction. In this work, we propose a …

This paper tackles the problem of localizing actions in long untrimmed videos. Different from
existing works, which all use annotated untrimmed videos during training, we learn only from …

Zero-shot action recognition is the task of classifying action categories that are not available
in the training set. In this setting, the standard evaluation protocol is to use existing action …

The major disadvantage of supervised methods for action recognition is the need for a large
amount of annotated data, where the data is matched to its label accurately. To address this …