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

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2025

CasP: Improving Semi-Dense Feature Matching Pipeline Leveraging Cascaded Correspondence Priors for Guidance

Peiqi Chen, Lei Yu, Yi Wan +9

Semi-dense feature matching methods have shown strong performance in challenging scenarios. However, the existing pipeline relies on a global search across the entire feature map t…

cs.CV2025

SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing

Yingying Zhang, Lixiang Ru, Kang Wu +4

The multi-modal remote sensing foundation model (MM-RSFM) has significantly advanced various Earth observation tasks, such as urban planning, environmental monitoring, and natural…

cs.AI2025

M2-Reasoning: Empowering MLLMs with Unified General and Spatial Reasoning

Inclusion AI, :, Fudong Wang +12

Recent advancements in Multimodal Large Language Models (MLLMs), particularly through Reinforcement Learning with Verifiable Rewards (RLVR), have significantly enhanced their reaso…

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.AI20251 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…

cs.CV2025

Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings

Panwang Xia, Lei Yu, Yi Wan +12

Cross-View Geo-Localization tackles the challenge of image geo-localization in GNSS-denied environments, including disaster response scenarios, urban canyons, and dense forests, by…