4 citations · 11 across the 6 of their papers we have counts for
6 papers
Clustered-patch Element Connection for Few-shot Learning
Jinxiang Lai, Siqian Yang, Junhong Zhou +5
Weak feature representation problem has influenced the performance of few-shot classification task for a long time. To alleviate this problem, recent researchers build connections…
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Wu +8
Recent Few-Shot Learning (FSL) methods put emphasis on generating a discriminative embedding features to precisely measure the similarity between support and query sets. Current CN…
Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning
Chengming Xu, Chen Liu, Siqian Yang +4
Positive-Unlabeled (PU) learning aims to learn a model with rare positive samples and abundant unlabeled samples. Compared with classical binary classification, the task of PU lear…
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
Chengming Xu, Chen Liu, Xinwei Sun +4
The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It…
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
Jinxiang Lai, Siqian Yang, Guannan Jiang +9
Few-shot learning problem focuses on recognizing unseen classes given a few labeled images. In recent effort, more attention is paid to fine-grained feature embedding, ignoring the…
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Jinxiang Lai, Siqian Yang, Wenlong Liu +6
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples. M…