18 citations · 69 across the 9 of their papers we have counts for
9 papers
DSS-Prompt: Dynamic-Static Synergistic Prompting for Few-Shot Class-Incremental Learning
Linpu He, Yanan Li, Bingze Li +2
Learning from large-scale pre-trained models with strong generalization ability has shown remarkable success in a wide range of downstream tasks recently, but it is still underexpl…
Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation
Elvis Han Cui, Bingbin Li, Yanan Li +2
Data augmentation for deep learning benefits model training, image transformation, medical imaging analysis and many other fields. Many existing methods generate new samples from a…
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
Jing Xu, Xu Luo, Xinglin Pan +3
Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning metho…
Q-Net: Query-Informed Few-Shot Medical Image Segmentation
Qianqian Shen, Yanan Li, Jiyong Jin +1
Deep learning has achieved tremendous success in computer vision, while medical image segmentation (MIS) remains a challenge, due to the scarcity of data annotations. Meta-learning…
Dual Path Structural Contrastive Embeddings for Learning Novel Objects
Bingbin Li, Elvis Han Cui, Yanan Li +2
Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas. Recent research on either meta-learning based or transfer-learn…
Class-Incremental Few-Shot Object Detection
Pengyang Li, Yanan Li, Han Cui +1
Conventional detection networks usually need abundant labeled training samples, while humans can learn new concepts incrementally with just a few examples. This paper focuses on a…
Learning Robust Features with Incremental Auto-Encoders
Yanan Li, Donghui Wang
Automatically learning features, especially robust features, has attracted much attention in the machine learning community. In this paper, we propose a new method to learn non-lin…
Zero-Shot Learning with Generative Latent Prototype Model
Yanan Li, Donghui Wang
Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most e…
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
Yanan Li, Donghui Wang, Huanhang Hu +2
Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding sp…