Recent zero-shot learning (ZSL) approaches have integrated fine-grained analysis, ie, fine-
grained ZSL, to mitigate the commonly known seen/unseen domain bias and misaligned …

This article provides a novel framework for single-domain generalized object detection (ie,
Single-DGOD), where we are interested in learning and maintaining the semantic structures …

Skeleton-based zero-shot action recognition aims to recognize unknown human actions
based on the learned priors of the known skeleton-based actions and a semantic descriptor …

Malware open-set recognition (MOSR) aims at jointly classifying malware samples from
known families and detect the ones from novel unknown families, respectively. Existing …

This paper investigates a challenging problem of zero-shot learning in the multi-label
scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within …

Federated learning (FL) has emerged as a promising privacy-preserving distributed
machine learning framework recently. It aims at collaboratively learning a shared global …

This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL),
dubbed ParsNets, where we are interested in learning a composition of on-device friendly …

Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing
a series of unbiased visual-semantics mappings, wherein, the precision relies heavily on the …

Federated learning (FL) is an emerging technique that trains massive and geographically
distributed edge data while maintaining privacy. However, FL has inherent challenges in …

We study the challenging task of malware recognition on both known and novel unknown
malware families, called malware open-set recognition (MOSR). Previous works usually …