26 citations · 62 across the 18 of their papers we have counts for
9 papers · 1 filter
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…
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…
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,…
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…
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…