2 citations · 3 across the 20 of their papers we have counts for
21 papers
From "What-If" to "What-Is": Counterfactual Thinking-Inspired Semantic Alignment for Visual Brain Decoding
Kaitao Yan, Chi Liu, Congcong Zhu +5
Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to studying how visual information…
Advantage-Guided Gate: Reshaping Open-Ended Reasoning for Vision-Based Spatial Intelligence
Ling Lin, Yang Bai, Congcong Zhu +6
Multimodal large language models (MLLMs) have demonstrated significant potential in complex spatial scene understanding and reasoning tasks. However, their open-ended reasoning pro…
One Video, One World: Turning Monocular Video into Physical 4D Scenes
Junhao Chen, Boran Zhang, Mingjin Chen +7
We introduce \textbf{OVOW}, the first training-free system that reconstructs \emph{instance-level, simulation-ready} 4D mesh scenes from a single monocular video. Recent 4D reconst…
MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
Yanshun Zhao, Xiaoyu Peng, Jiamin Jiang +2
Conventional patchified Transformers operate on uniform spatial partitions, distributing computational effort evenly across the domain irrespective of local features. This inflexib…
When LLMs Team Up: A Coordinated Attack Framework for Automated Cyber Intrusions
Minfeng Qi, Tianqing Zhu, Zijie Xu +3
Automated intrusion-style workflows require LLM agents to reason over partial observations, tool outputs, and executable artifacts under bounded budgets. A single LLM instance ofte…
Learning to Look before Learning to Like: Incorporating Human Visual Cognition into Aesthetic Quality Assessment
Liwen Yu, Chi Liu, Xiaotong Han +3
Automated Aesthetic Quality Assessment (AQA) treats images primarily as static pixel vectors, aligning predictions with human-rating scores largely through semantic perception. How…
Are LLMs Ready for Computer Science Education? A Cross-Domain, Cross-Lingual and Cognitive-Level Evaluation Using Professional Certification Exams
Chen Gao, Chi Liu, Zhengquan Luo +10
Large language models (LLMs) are increasingly applied in computer science education for tasks such as tutoring, content generation, and code assessment. However, systematic evaluat…
Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents
Dayong Ye, Tainqing Zhu, Congcong Zhu +5
Large language model (LLM)-based agents have recently gained considerable attention due to the powerful reasoning capabilities of LLMs. Existing research predominantly focuses on e…
From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration
Yizhe Xie, Congcong Zhu, Xinyue Zhang +5
Large Language Model-based Multi-Agent Systems (LLM-MAS) are increasingly applied to complex collaborative scenarios. However, their collaborative mechanisms may cause minor inaccu…
OODBench: Out-of-Distribution Benchmark for Large Vision-Language Models
Ling Lin, Yang Bai, Heng Su +5
Existing Visual-Language Models (VLMs) have achieved significant progress by being trained on massive-scale datasets, typically under the assumption that data are independent and i…
Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field
Haoqin Hong, Ding Fan, Fubin Dou +4
Recently, 3D Gaussian Splatting (3DGS), an explicit scene representation technique, has shown significant promise for dynamic novel-view synthesis from monocular video input. Howev…
Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Method
Chi Liu, Jincheng Liu, Congcong Zhu +5
Developing Medical AI relies on large datasets and easily suffers from data scarcity. Generative data augmentation (GDA) using AI generative models offers a solution to synthesize…
Causal Fingerprints of AI Generative Models
Hui Xu, Chi Liu, Congcong Zhu +3
AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for source attribution. Prior methods r…
Stage-wise Adaptive Label Distribution for Facial Age Estimation
Bo Wu, Zhiqi Ai, Jun Jiang +2
Label ambiguity poses a significant challenge in age estimation tasks. Most existing methods address this issue by modeling correlations between adjacent age groups through label d…
Who's the Mole? Modeling and Detecting Intention-Hiding Malicious Agents in LLM-Based Multi-Agent Systems
Yizhe Xie, Congcong Zhu, Xinyue Zhang +4
Multi-agent systems powered by Large Language Models (LLM-MAS) have demonstrated remarkable capabilities in collaborative problem-solving. However, their deployment also introduces…
Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models
Congcong Zhu, Xiaoyan Xu, Jiayue Han +1
Auto-regressive partial differential equation (PDE) foundation models have shown great potential in handling time-dependent data. However, these models suffer from the shortcut pro…
STSA: Spatial-Temporal Semantic Alignment for Visual Dubbing
Zijun Ding, Mingdie Xiong, Congcong Zhu +1
Existing audio-driven visual dubbing methods have achieved great success. Despite this, we observe that the semantic ambiguity between spatial and temporal domains significantly de…
Reinforcement Unlearning
Dayong Ye, Tianqing Zhu, Congcong Zhu +6
Machine unlearning refers to the process of mitigating the influence of specific training data on machine learning models based on removal requests from data owners. However, one i…
Reasoning Structural Relation for Occlusion-Robust Facial Landmark Localization
Congcong Zhu, Xiaoqiang Li, Jide Li +2
In facial landmark localization tasks, various occlusions heavily degrade the localization accuracy due to the partial observability of facial features. This paper proposes a struc…
Unsupervised Learning of Multi-level Structures for Anomaly Detection
Songmin Dai, Jide Li, Lu Wang +3
The main difficulty in high-dimensional anomaly detection tasks is the lack of anomalous data for training. And simply collecting anomalous data from the real world, common distrib…
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos
Congcong Zhu, Hao Liu, Zhenhua Yu +1
In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos. Unlike existing…