most citedMiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction

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

collaborators

9 papers

cs.CL20261 cited

MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction

Junbo Cui, Bokai Xu, Chongyi Wang +33

Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming interaction, yet they still remai…

cs.CV2026

CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition

Ying Yu, Siyao Li, Yixuan Jiang +5

Human Activity Recognition (HAR) is a fundamental technology for numerous human - centered intelligent applications. Although deep learning methods have been utilized to accelerate…

cs.CV2025

Motus: A Unified Latent Action World Model

Hongzhe Bi, Hengkai Tan, Shenghao Xie +13

While a general embodied agent must function as a unified system, current methods are built on isolated models for understanding, world modeling, and control. This fragmentation pr…

cs.LG2025

Generative AI Meets Wireless Sensing: Towards Wireless Foundation Model

Zheng Yang, Guoxuan Chi, Chenshu Wu +5

Generative Artificial Intelligence (GenAI) has made significant advancements in fields such as computer vision (CV) and natural language processing (NLP), demonstrating its capabil…

cs.LG2025

MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Tianyu Yu, Zefan Wang, Chongyi Wang +31

Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged a…

cs.CV2025

Confidence-driven Gradient Modulation for Multimodal Human Activity Recognition: A Dynamic Contrastive Dual-Path Learning Approach

Panpan Ji, Junni Song, Yifan Lu +3

Sensor-based Human Activity Recognition (HAR) is a core technology that enables intelligent systems to perceive and interact with their environment. However, multimodal HAR systems…