41 citations · 42 across the 8 of their papers we have counts for
9 papers
More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Linhao Zhong, Bozhen Fang, Xuehao Wang +7
Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published baseline scores under differing ex…
Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding
Hongyu Qu, Guangming Yao, Ling Xing +7
Streaming video understanding requires multimodal large language models (MLLMs) to process continuous visual inputs and respond to user queries under strict causality and bounded m…
VideoGAIA: A Benchmark for General AI Assistants on Agentic Video Understanding
Fan Zhang, Guangming Yao, Jinyang Wu +6
Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approx…
MemDreamer: Decoupling Perception and Reasoning for Long Video Understanding via Hierarchical Graph Memory and Agentic Retrieval Mechanism
Cong Chen, Guo Gan, Kaixiang Ji +7
Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and attention dilution. To overco…
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…
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…
Learning Implicit Body Representations from Double Diffusion Based Neural Radiance Fields
Guangming Yao, Hongzhi Wu, Yi Yuan +3
In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel vie…
One-shot Face Reenactment Using Appearance Adaptive Normalization
Guangming Yao, Yi Yuan, Tianjia Shao +5
The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a dri…
Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks
Guangming Yao, Yi Yuan, Tianjia Shao +1
Face reenactment aims to animate a source face image to a different pose and expression provided by a driving image. Existing approaches are either designed for a specific identity…