5 papers
Aligned Better, Listen Better for Audio-Visual Large Language Models
Yuxin Guo, Shuailei Ma, Shijie Ma +7
Audio is essential for multimodal video understanding. On the one hand, video inherently contains audio, which supplies complementary information to vision. Besides, video large la…
Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
Nianzu Yang, Pandeng Li, Liming Zhao +8
Existing video tokenizers typically use the traditional Variational Autoencoder (VAE) architecture for video compression and reconstruction. However, to achieve good performance, i…
Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
Zhihang Liu, Chen-Wei Xie, Pandeng Li +5
Recent Multi-modal Large Language Models (MLLMs) have been challenged by the computational overhead resulting from massive video frames, often alleviated through compression strate…
ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer
Zhen Han, Zeyinzi Jiang, Yulin Pan +5
Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primari…
ESND: An Embedding-based Framework for Signed Network Dismantling
Chenwei Xie, Chuang Liu, Cong Li +2
Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracki…