1 citations · 1 across the 5 of their papers we have counts for
5 papers
HIFICL: High-Fidelity In-Context Learning for Multimodal Tasks
Xiaoyu Li, Yuhang Liu, Xuanshuo Kang +4
In-Context Learning (ICL) is a significant paradigm for Large Multimodal Models (LMMs), using a few in-context demonstrations (ICDs) for new task adaptation. However, its performan…
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…
MoCHA: Advanced Vision-Language Reasoning with MoE Connector and Hierarchical Group Attention
Yuqi Pang, Bowen Yang, Yun Cao +3
Vision large language models (VLLMs) are focusing primarily on handling complex and fine-grained visual information by incorporating advanced vision encoders and scaling up visual…
Ming-Omni: A Unified Multimodal Model for Perception and Generation
Inclusion AI, Biao Gong, Cheng Zou +55
We propose Ming-Omni, a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation. M…
A Policy-based Approach to the SpecAugment Method for Low Resource E2E ASR
Rui Li, Guodong Ma, Dexin Zhao +3
SpecAugment is a very effective data augmentation method for both HMM and E2E-based automatic speech recognition (ASR) systems. Especially, it also works in low-resource scenarios.…