13 citations · 22 across the 6 of their papers we have counts for
7 papers
MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations
Wenxiang Guo, Changhao Pan, Zhiyuan Zhu +16
Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role…
Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation
Inclusion AI, :, Bowen Ma +73
We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which on…
TechSinger: Technique Controllable Multilingual Singing Voice Synthesis via Flow Matching
Wenxiang Guo, Yu Zhang, Changhao Pan +6
Singing voice synthesis has made remarkable progress in generating natural and high-quality voices. However, existing methods rarely provide precise control over vocal techniques s…
MoMu-Diffusion: On Learning Long-Term Motion-Music Synchronization and Correspondence
Fuming You, Minghui Fang, Li Tang +3
Motion-to-music and music-to-motion have been studied separately, each attracting substantial research interest within their respective domains. The interaction between human motio…
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
Haoyi Duan, Yan Xia, Mingze Zhou +3
In recent years, the deployment of large-scale pre-trained models in audio-visual downstream tasks has yielded remarkable outcomes. However, these models, primarily trained on sing…
Gloss Attention for Gloss-free Sign Language Translation
Aoxiong Yin, Tianyun Zhong, Li Tang +3
Most sign language translation (SLT) methods to date require the use of gloss annotations to provide additional supervision information, however, the acquisition of gloss is not ea…
Connecting Multi-modal Contrastive Representations
Zehan Wang, Yang Zhao, Xize Cheng +8
Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability…