70 citations · 501 across the 62 of their papers we have counts for
13 papers · 1 filter
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
Jie Wen, Zheng Zhang, Yong Xu +3
In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a…
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Yadan Luo, Zhuoxiao Chen, Zhen Fang +3
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (A…
Side Adapter Network for Open-Vocabulary Semantic Segmentation
Mengde Xu, Zheng Zhang, Fangyun Wei +2
This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN). Our approach models the…
Self-Healing Robust Neural Networks via Closed-Loop Control
Zhuotong Chen, Qianxiao Li, Zheng Zhang
Despite the wide applications of neural networks, there have been increasing concerns about their vulnerability issue. While numerous attack and defense techniques have been develo…
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
Zhi Chen, Zi Huang, Jingjing Li +1
Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise bot…
Active Sampling for Accelerated MRI with Low-Rank Tensors
Zichang He, Bo Zhao, Zheng Zhang
Magnetic resonance imaging (MRI) is a powerful imaging modality that revolutionizes medicine and biology. The imaging speed of high-dimensional MRI is often limited, which constrai…
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
Zhenda Xie, Yutong Lin, Zheng Zhang +3
Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning…
Adversarial Bipartite Graph Learning for Video Domain Adaptation
Yadan Luo, Zi Huang, Zijian Wang +2
Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant sp…
RepPoints V2: Verification Meets Regression for Object Detection
Yihong Chen, Zheng Zhang, Yue Cao +3
Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regress…
A Closer Look at Local Aggregation Operators in Point Cloud Analysis
Ze Liu, Han Hu, Yue Cao +2
Recent advances of network architecture for point cloud processing are mainly driven by new designs of local aggregation operators. However, the impact of these operators to networ…
Parametric Instance Classification for Unsupervised Visual Feature Learning
Yue Cao, Zhenda Xie, Bin Liu +3
This paper presents parametric instance classification (PIC) for unsupervised visual feature learning. Unlike the state-of-the-art approaches which do instance discrimination in a…
Disentangled Non-Local Neural Networks
Minghao Yin, Zhuliang Yao, Yue Cao +4
The non-local block is a popular module for strengthening the context modeling ability of a regular convolutional neural network. This paper first studies the non-local block in de…
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
Jie Wen, Zheng Zhang, Yong Xu +1
Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specifically, the proposed meth…