13 citations · 18 across the 5 of their papers we have counts for
5 papers
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning
Jinze Li, Yan Bai, Yihang Lou +4
Few-shot class-incremental learning(FSCIL) focuses on designing learning algorithms that can continually learn a sequence of new tasks from a few samples without forgetting old one…
Bayesian Evidential Learning for Few-Shot Classification
Xiongkun Linghu, Yan Bai, Yihang Lou +4
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like mach…
Switchable Representation Learning Framework with Self-compatibility
Shengsen Wu, Yan Bai, Yihang Lou +3
Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrai…
T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation
Ruihuang Li, Xu Jia, Jianzhong He +2
Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help…
Multi-Target Domain Adaptation with Collaborative Consistency Learning
Takashi Isobe, Xu Jia, Shuaijun Chen +5
Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However…