197 citations · 279 across the 11 of their papers we have counts for
11 papers
Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback
Yang Chen, Yufan Shen, Wenxuan Huang +7
Multimodal Large Language Models (MLLMs) exhibit impressive performance across various visual tasks. Subsequent investigations into enhancing their visual reasoning abilities have…
SAM-SP: Self-Prompting Makes SAM Great Again
Chunpeng Zhou, Kangjie Ning, Qianqian Shen +3
The recently introduced Segment Anything Model (SAM), a Visual Foundation Model (VFM), has demonstrated impressive capabilities in zero-shot segmentation tasks across diverse natur…
Towards a Unified Framework of Clustering-based Anomaly Detection
Zeyu Fang, Ming Gu, Sheng Zhou +4
Unsupervised Anomaly Detection (UAD) plays a crucial role in identifying abnormal patterns within data without labeled examples, holding significant practical implications across v…
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Jinke Shi +3
Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real…
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
Chunpeng Zhou, Kangjie Ning, Haishuai Wang +3
The application of 3D ground-penetrating radar (3D-GPR) for subgrade distress detection has gained widespread popularity. To enhance the efficiency and accuracy of detection, pione…
Homophily-enhanced Structure Learning for Graph Clustering
Ming Gu, Gaoming Yang, Sheng Zhou +5
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existi…
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Yuanchen Bei, Hao Xu, Sheng Zhou +5
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the adva…
Hilbert Distillation for Cross-Dimensionality Networks
Dian Qin, Haishuai Wang, Zhe Liu +3
3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leverag…
Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning Strategy
Jingru Li, Sheng Zhou, Liangcheng Li +3
Data-free knowledge distillation (DFKD) is a widely-used strategy for Knowledge Distillation (KD) whose training data is not available. It trains a lightweight student model with t…
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Sheng Zhou, Hongjia Xu, Zhuonan Zheng +7
Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as fea…
Efficient Medical Image Segmentation Based on Knowledge Distillation
Dian Qin, Jiajun Bu, Zhe Liu +6
Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of…