The advancement of audio-language (AL) multimodal learning tasks has been significant in
recent years, yet the limited size of existing audio-language datasets poses challenges for …

Audio-language models (ALMs) generate linguistic descriptions of sound-producing events
and scenes. Advances in dataset creation and computational power have led to significant …

Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio
and exhibits promising performance in several classification tasks. However, conventional …

Contrastive language-audio pretraining (CLAP) has been developed to align the
representations of audio and language, achieving remarkable performance in retrieval and …

In this paper, we aim to establish an automatic, scalable pipeline for denoising the large-
scale instructional dataset and construct a high-quality video-text dataset with multiple …

Visual and auditory perception are two crucial ways humans experience the world. Text-to-
video generation has made remarkable progress over the past year, but the absence of …

Generative models have shown significant achievements in audio generation tasks.
However, existing models struggle with complex and detailed prompts, leading to potential …

Large language models (LLMs) have demonstrated remarkable capabilities in natural
language and multimodal domains. By fine-tuning multimodal LLMs with temporal …

Endeavors have been made to explore Large Language Models for video analysis (Video-
LLMs), particularly in understanding and interpreting long videos. However, existing Video …

Recent advances in audio understanding tasks leverage the reasoning capabilities of LLMs.
However, adapting LLMs to learn audio concepts requires massive training data and …