47 citations · 88 across the 13 of their papers we have counts for
5 papers · 1 filter
From Local Binary Patterns to Pixel Difference Networks for Efficient Visual Representation Learning
Zhuo Su, Matti Pietikäinen, Li Liu
LBP is a successful hand-crafted feature descriptor in computer vision. However, in the deep learning era, deep neural networks, especially convolutional neural networks (CNNs) can…
Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters
Yawen Cui, Zitong Yu, Rizhao Cai +3
The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current F…
Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning
Yawen Cui, Wanxia Deng, Haoyu Chen +1
Given a model well-trained with a large-scale base dataset, Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by a…
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Yawen Cui, Zitong Yu, Wei Peng +1
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultan…
Decoupling Makes Weakly Supervised Local Feature Better
Kunhong Li, Longguang Wang, Li Liu +3
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supe…