With only video-level event labels, this paper targets at the task of weakly-supervised audio-
visual event perception (WS-AVEP), which aims to temporally localize and categorize events …

With the explosive growth of videos, weakly-supervised temporal action localization (WS-
TAL) task has become a promising research direction in pattern analysis and machine …

Multi-view learning aims to combine multiple features to achieve more comprehensive
descriptions of data. Most previous works assume that multiple views are strictly aligned …

Multimodal fusion focuses on integrating information from multiple modalities with the goal of
more accurate prediction, which has achieved remarkable progress in a wide range of …

Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic
knowledge transfer from known to unknown categories. This knowledge, typically …

Targeting at recognizing and localizing action instances with only video-level labels during
training, Weakly-supervised Temporal Action Localization (WTAL) has achieved significant …

Multi-view deep classification expects to obtain better classification performance than using
a single view. However, due to the uncertainty and inconsistency of data sources, adding …

The multi-modality characteristic of medical images calls for the application of information
fusion theory in computer aided diagnosis (CAD) algorithm design. Recently, the research of …

Current research suggests that there exist certain limitations in EEG emotion recognition,
including redundant and meaningless time-frames and channels, as well as inter-and intra …

Abstract Fine-Grained Image Classification (FGIC) aims to classify images into specific
subordinate classes of a superclass. Due to insufficient training data and confusing data …