1 citations · 1 across the 12 of their papers we have counts for
15 papers
GeONet: Geometry-Encoded Operator Network for Predicting Flow in Heterogeneous Porous Media
Md Lal Mamud, Maruti K. Mudunuru, Yucheng Fu +13
Rapid prediction of pressure-driven flow in microfluidic porous media is important for experimental design, tracer-test interpretation, and digital-twin development for subsurface…
ICM-Bench: Person-Level Identity Reasoning in Multimodal Agents with Long-Term Memory
Shidu Ren, Yunze Liu, Xing Liu +3
Long-horizon multimodal agents should remember not only what happened but also who participated. This capability depends on linking recurring faces, voices, names, person-associate…
OmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation
Yunze Liu, Chi-Hao Wu, Enmin Zhou +1
Unified multimodal embedding spaces have become the standard interface for cross-modal retrieval and multimodal RAG, and recent audio-video-text (AVT) encoders extend this setting…
O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding
Peiran Wu, Yunze Liu, Chi-Hao Wu +2
Omnimodal large language models enable unified audio video understanding, but long joint token sequences make inference costly, and existing benchmarks do not fully isolate audio v…
Bridging Modalities, Spanning Time: Structured Memory for Ultra-Long Agentic Video Reasoning
Jiazheng Li, Chi-Hao Wu, Yunze Liu +3
Understanding ultra-long videos such as egocentric recordings, live streams, or surveillance footage spanning days to weeks, remains a challenge. For current multimodal LLMs: even…
NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)
Guanyi Qin, Jie Liang, Bingbing Zhang +50
In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Asses…
SpatialMem: Metric-Aligned Long-Horizon Video Memory for Language Grounding and QA
Xinyi Zheng, Yunze Liu, Chi-Hao Wu +5
SpatialMem is a memory-based system which integrates 3D geometry, semantics, and language with one another in one combined, queryable representation. Starting with casually recorde…
PointNet4D: A Lightweight 4D Point Cloud Video Backbone for Online and Offline Perception in Robotic Applications
Yunze Liu, Zifan Wang, Peiran Wu +1
Understanding dynamic 4D environments-3D space evolving over time-is critical for robotic and interactive systems. These applications demand systems that can process streaming poin…
MARC: Memory-Augmented RL Token Compression for Efficient Video Understanding
Peiran Wu, Zhuorui Yu, Yunze Liu +3
The rapid progress of large language models (LLMs) has laid the foundation for multimodal models. However, visual language models (VLMs) still face heavy computational costs when e…
OnlineHOI: Towards Online Human-Object Interaction Generation and Perception
Yihong Ji, Yunze Liu, Yiyao Zhuo +4
The perception and generation of Human-Object Interaction (HOI) are crucial for fields such as robotics, AR/VR, and human behavior understanding. However, current approaches model…
UGC-VideoCaptioner: An Omni UGC Video Detail Caption Model and New Benchmarks
Peiran Wu, Yunze Liu, Zhengdong Zhu +2
Real-world user-generated videos, especially on platforms like TikTok, often feature rich and intertwined audio visual content. However, existing video captioning benchmarks and mo…
MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory
Zifan Wang, Jingwei Li, Yitang Li +1
This paper introduces MutualNeRF, a framework enhancing Neural Radiance Field (NeRF) performance under limited samples using Mutual Information Theory. While NeRF excels in 3D scen…
ST-Think: How Multimodal Large Language Models Reason About 4D Worlds from Ego-Centric Videos
Peiran Wu, Yunze Liu, Miao Liu +1
Humans excel at spatial-temporal reasoning, effortlessly interpreting dynamic visual events from an egocentric viewpoint. However, whether multimodal large language models (MLLMs)…
VideoMAP: Toward Scalable Mamba-based Video Autoregressive Pretraining
Yunze Liu, Peiran Wu, Cheng Liang +3
Recent Mamba-based architectures for video understanding demonstrate promising computational efficiency and competitive performance, yet struggle with overfitting issues that hinde…
CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering
Xinyi Zheng, Steve Zhang, Weizhe Lin +4
Current state-of-the-art 3D reconstruction models face limitations in building extra-large scale outdoor scenes, primarily due to the lack of sufficiently large-scale and detailed…