Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …

Continual learning is a challenging problem in which models need to be trained on non-
stationary data across sequential tasks for class-incremental learning. While previous …

Audio-visual segmentation is a challenging task that aims to predict pixel-level masks for
sound sources in a video. Previous work applied a comprehensive manually designed …

Humans possess a remarkable ability to integrate auditory and visual information enabling a
deeper understanding of the surrounding environment. This early fusion of audio and visual …

The joint analysis of audio and video is a powerful tool that can be applied to various
contexts, including action, speech, and sound recognition, audio-visual video parsing …

This work explores class-incremental learning (CIL) for sound event detection (SED),
advancing adaptability towards real-world scenarios. CIL's success in domains like …

Video-language pre-training is a typical and challenging problem that aims at learning
visual and textual representations from large-scale data in a self-supervised way. Existing …

Audio-visual question answering (AVQA) requires reference to video content and auditory
information, followed by correlating the question to predict the most precise answer …

Recently, neural networks have shown impressive progress across diverse fields, with
speech processing being no exception. However, recent breakthroughs in this area require …

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 …