12 citations · 42 across the 8 of their papers we have counts for
8 papers
From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics
Qinshuo Liu, Weiqin Zhao, Wei Huang +3
The depth of neural networks is a critical factor for their capability, with deeper models often demonstrating superior performance. Motivated by this, significant efforts have bee…
MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps
Xue Xia, Daiwei Zhang, Wenxuan Song +2
Automated feature detection in historical maps can significantly accelerate the reconstruction of the geospatial past. However, this process is often constrained by the time-consum…
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou +3
A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead o…
Generic-to-Specific Distillation of Masked Autoencoders
Wei Huang, Zhiliang Peng, Li Dong +3
Large vision Transformers (ViTs) driven by self-supervised pre-training mechanisms achieved unprecedented progress. Lightweight ViT models limited by the model capacity, however, b…
HiViT: Hierarchical Vision Transformer Meets Masked Image Modeling
Xiaosong Zhang, Yunjie Tian, Wei Huang +4
Recently, masked image modeling (MIM) has offered a new methodology of self-supervised pre-training of vision transformers. A key idea of efficient implementation is to discard the…
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
Yunjie Tian, Lingxi Xie, Xiaopeng Zhang +6
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by…
Long-tailed Distribution Adaptation
Zhiliang Peng, Wei Huang, Zonghao Guo +3
Recognizing images with long-tailed distributions remains a challenging problem while there lacks an interpretable mechanism to solve this problem. In this study, we formulate Long…
Conformer: Local Features Coupling Global Representations for Visual Recognition
Zhiliang Peng, Wei Huang, Shanzhi Gu +4
Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visu…