most citedMing-Omni: A Unified Multimodal Model for Perception and Generation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Bridging Day and Night: Unsupervised Cross-Domain Re-Identification with Synergistic Prompt and Prototype Learning

Jiyang Xu, Rui Liu, Hang Dai

Cross-domain day-night re-identification (ReID) is fundamentally challenged by the substantial visual appearance discrepancies between daytime and nighttime scenes. Existing fully…

cs.CV2025

Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer

Ziyuan Huang, DanDan Zheng, Cheng Zou +13

Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in dis…

cs.CV2025

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…

cs.CV2025

Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction

Inclusion AI, Biao Gong, Cheng Zou +14

We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifyi…

cs.AI2025★ 1 cited

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…