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

Deep representation learning is a ubiquitous part of modern computer vision. While
Euclidean space has been the de facto standard manifold for learning visual …

Audio-visual generalized zero-shot learning is a rapidly advancing domain that seeks to
understand the intricate relations between audio and visual cues within videos. The …

While recent vision-and-language models (VLMs) like CLIP are a powerful tool for analyzing
text and images in a shared semantic space, they do not explicitly model the hierarchical …

Audio-visual zero-shot learning methods commonly build on features extracted from pre-
trained models eg video or audio classification models. However existing benchmarks …

Audio-visual zero-shot learning aims to recognize unseen categories based on paired audio-
visual sequences. Recent methods mainly focus on learning aligned and discriminative …

Automatically understanding funny moments (ie, the moments that make people laugh)
when watching comedy is challenging, as they relate to various features, such as body …

Audio-visual zero-shot learning (ZSL) leverages both video and audio information for model
training, aiming to classify new video categories that were not seen during the training …

Reconstructing desired objects and scenes has long been a primary goal in 3D computer
vision. Single-view point cloud reconstruction has become a popular technique due to its …

Audio-visual Zero-Shot Learning (ZSL) has attracted significant attention for its ability to
identify unseen classes and perform well in video classification tasks. However, modal …