257 citations · 437 across the 40 of their papers we have counts for
10 papers · 1 filter
RhythmFormer: Extracting Patterned rPPG Signals based on Periodic Sparse Attention
Bochao Zou, Zizheng Guo, Jiansheng Chen +3
Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals based on facial videos, holding high potential in various applications. Due to the pe…
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification
Kaipeng Zheng, Weiran Huang, Lichao Sun
Few-shot learning has been studied to adapt models to tasks with very few samples. It holds profound significance, particularly in clinical tasks, due to the high annotation cost o…
FD-Align: Feature Discrimination Alignment for Fine-tuning Pre-Trained Models in Few-Shot Learning
Kun Song, Huimin Ma, Bochao Zou +2
Due to the limited availability of data, existing few-shot learning methods trained from scratch fail to achieve satisfactory performance. In contrast, large-scale pre-trained mode…
AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering
Xiuyuan Chen, Yuan Lin, Yuchen Zhang +1
We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively evaluate large vision-language models in open-ended video question answering. The comprehensiveness…
OTMatch: Improving Semi-Supervised Learning with Optimal Transport
Zhiquan Tan, Kaipeng Zheng, Weiran Huang
Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled d…
Information Flow in Self-Supervised Learning
Zhiquan Tan, Jingqin Yang, Weiran Huang +2
In this paper, we conduct a comprehensive analysis of two dual-branch (Siamese architecture) self-supervised learning approaches, namely Barlow Twins and spectral contrastive learn…
DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Kaipeng Zheng, Huishuai Zhang, Weiran Huang
Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories were not seen by the model before. This often leads to a relatively uniform d…
Boosting Few-Shot Learning With Adaptive Margin Loss
Aoxue Li, Weiran Huang, Xu Lan +3
Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples. T…
DARTS+: Improved Differentiable Architecture Search with Early Stopping
Hanwen Liang, Shifeng Zhang, Jiacheng Sun +4
Recently, there has been a growing interest in automating the process of neural architecture design, and the Differentiable Architecture Search (DARTS) method makes the process ava…
Few-Shot Learning with Global Class Representations
Tiange Luo, Aoxue Li, Tao Xiang +2
In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples. In ea…