26 citations · 62 across the 18 of their papers we have counts for
18 papers
Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification
Shu Shen, C. L. Philip Chen, Tong Zhang
Reliable learning of multimodal data (e.g., multi-omics) is a widely concerning issue, especially in safety-critical applications such as medical diagnosis. However, low-quality da…
AIM: Adaptive Intra-Network Modulation for Balanced Multimodal Learning
Shu Shen, C. L. Philip Chen, Tong Zhang
Multimodal learning has significantly enhanced machine learning performance but still faces numerous challenges and limitations. Imbalanced multimodal learning is one of the proble…
CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations
Puchun Liu, C. L. Philip Chen, Yubin He +1
The difficulty of extracting deep features from EEG data and effectively integrating information from multiple views presents significant challenges for developing a generalizable…
FACE: Few-shot Adapter with Cross-view Fusion for Cross-subject EEG Emotion Recognition
Haiqi Liu, C. L. Philip Chen, Tong Zhang
Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addr…
Reliable Multimodal Learning Via Multi-Level Adaptive DeConfusion
Tong Zhang, Shu Shen, C. L. Philip Chen
Multimodal learning enhances the performance of various machine learning tasks by leveraging complementary information across different modalities. However, existing methods often…
DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
Jiaxin Guo, C. L. Philip Chen, Shuzhen Li +1
Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. It provides high-quality data at a low annotation cost for label-scarc…
Online-BLS: An Accurate and Efficient Online Broad Learning System for Data Stream Classification
Chunyu Lei, Guang-Ze Chen, C. L. Philip Chen +1
The state-of-the-art online learning models generally conduct a single online gradient descent when a new sample arrives and thus suffer from suboptimal model weights. To this end,…
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
Pengyu Li, Zhijie Zhong, Tong Zhang +3
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction…
Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification
Shu Shen, C. L. Philip Chen, Tong Zhang
Multimodal machine learning has achieved remarkable progress in many scenarios, but its reliability is undermined by varying sample quality. This paper finds that existing reliable…
PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance
Haohan Weng, Yikai Wang, Tong Zhang +2
Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation,…
Incremental Self-training for Semi-supervised Learning
Jifeng Guo, Zhulin Liu, Tong Zhang +1
Semi-supervised learning provides a solution to reduce the dependency of machine learning on labeled data. As one of the efficient semi-supervised techniques, self-training (ST) ha…
Desigen: A Pipeline for Controllable Design Template Generation
Haohan Weng, Danqing Huang, Yu Qiao +4
Templates serve as a good starting point to implement a design (e.g., banner, slide) but it takes great effort from designers to manually create. In this paper, we present Desigen,…
Consistent123: Improve Consistency for One Image to 3D Object Synthesis
Haohan Weng, Tianyu Yang, Jianan Wang +4
Large image diffusion models enable novel view synthesis with high quality and excellent zero-shot capability. However, such models based on image-to-image translation have no guar…
Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition
Haiqi Liu, C. L. Philip Chen, Xinrong Gong +1
Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing lo…
ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification
Chunyu Lei, C. L. Philip Chen, Jifeng Guo +1
Deep learning generally suffers from enormous computational resources and time-consuming training processes. Broad Learning System (BLS) and its convolutional variants have been pr…
A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition
Jibao Qiu, C. L. Philip Chen, Tong Zhang
Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. Previous work mainly focused on learning better representation v…
OneDConv: Generalized Convolution For Transform-Invariant Representation
Tong Zhang, Haohan Weng, Ke Yi +1
Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks. However, the lack of transform-invariant property limits their further applicati…
Siamese Labels Auxiliary Learning
Wenrui Gan, Zhulin Liu, C. L. Philip Chen +1
In deep learning, auxiliary training has been widely used to assist the training of models. During the training phase, using auxiliary modules to assist training can improve the pe…